Adaptive Natural Experiment

adaptive quasi-experiment
adaptive exogenous shock design
adaptive as-if randomization
sequential natural experiment

2000s–2010s (systematic application in policy and social science evaluation)

Synthesizes natural experiment tradition (Meyer 1995; Dunning 2012) with adaptive design principles (Wald 1947; Chow & Chang 2008)

An adaptive natural experiment combines the causal logic of the natural experiment — exploiting real-world events that assign individuals to conditions in a plausibly exogenous way — with pre-specified adaptive monitoring rules that allow the analytic protocol to be modified based on accumulating data. This hybrid design is used in economics, epidemiology, and policy evaluation when the natural event unfolds over time and interim evidence can legitimately inform decisions about data collection scope, subgroup focus, or analytic strategy without compromising causal validity.

Quasi-experimental adaptive research design

Observational data with exogenous assignment mechanism; administrative, survey, or longitudinal records accumulated over time

Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.

Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC.

natural-experiment adaptive-experiment difference-in-differences regression-discontinuity-design instrumental-variable field-experiment


Adaptive nested case-control

adaptive NCC
adaptive nested case-referent study
dynamic nested case-control
sequential nested case-control

Base design 1977; adaptive extensions from 1990s onward

Nested case-control: D. C. Thomas (1977); adaptive design framework: Peter Bauer & Klaus Kohne (1994)

An adaptive nested case-control study embeds a case-control comparison within a defined cohort and incorporates pre-specified interim decision rules that allow modifications — such as control-to-case ratio adjustment or biomarker sub-sampling revision — based on accumulating data, without compromising the study's validity or inflating type I error. The design combines the efficiency of the nested case-control framework with the flexibility of adaptive methodology to optimise resource use when exposure assessment is costly.

Observational epidemiological study with adaptive design elements

Time-to-event data, exposure records, biospecimens drawn from a defined cohort

Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.

Bauer, P., & Kohne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics, 50(4), 1029–1041.

nested-case-control case-control-study adaptive-randomized-clinical-trial cohort-study adaptive-cohort-study case-crossover-design


Adaptive Phase I Clinical Trial

adaptive dose-escalation trial
adaptive dose-finding study
model-based adaptive Phase I design

1990 (model-based adaptive era); rule-based designs from the 1970s–1980s

O'Quigley, Pepe, and Fisher (CRM); earlier rule-based 3+3 designs pre-date it

An adaptive Phase I clinical trial is a first-in-human or early-phase dose-finding study that continuously updates the recommended dose after each patient cohort using a prespecified statistical model, rather than following a fixed rule. The goal is to identify the maximum tolerated dose (MTD) or the recommended Phase II dose (RP2D) efficiently while minimising exposure of participants to sub-therapeutic or toxic doses. Adaptive designs — most notably the Continual Reassessment Method (CRM) — replace or augment traditional rule-based designs such as the 3+3 schema.

Dose-toxicity binary or ordinal outcomes from successive patient cohorts

O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.

Chevret, S. (Ed.). (2006). Statistical Methods for Dose-Finding Experiments. Wiley.

continual-reassessment-method 3-plus-3-design dose-escalation-study bayesian-adaptive-design maximum-tolerated-dose-estimation adaptive-design


Adaptive Phase II Clinical Trial

Adaptive Ph II trial
seamless adaptive Phase II
adaptive dose-finding trial
response-adaptive Phase II

1994 (formal framework); widespread adoption 2000s–2010s

Peter Bauer & Klaus Kohne (formal statistical framework, 1994); broader adaptive trial methodology developed through FDA and ICH guidance in the 2000s

An adaptive Phase II clinical trial is a prospective experimental design in which pre-specified rules allow the study protocol to be modified — such as dropping arms, adjusting sample size, or narrowing the patient population — based on accumulating interim data, without inflating the Type I error rate. The design is widely used in early-phase drug development to screen candidate doses or treatments efficiently while preserving statistical validity.

Patient-level outcome data (binary, continuous, or time-to-event) collected prospectively during interim analyses

Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.

phase-ii-clinical-trial seamless-adaptive-design response-adaptive-randomization bayesian-adaptive-trial dose-finding-study group-sequential-design


Adaptive Phase III clinical trial

Adaptive Phase III Confirmatory Clinical Trial

adaptive confirmatory trial
seamless Phase II/III adaptive trial
adaptive pivotal trial
adaptive design Phase III

1969–2019 (sequential testing roots ~1969; formal adaptive design guidance 2010–2019)

Methodological foundations by Armitage et al. (1969); modern adaptive framework codified by FDA and ICH guidance (2010s)

An adaptive Phase III clinical trial is a confirmatory randomized controlled trial that incorporates pre-specified rules allowing modifications to the trial design — such as sample size re-estimation, dose selection, or population enrichment — based on accumulating interim data, while preserving the Type I error rate. It sits at the top of the evidence hierarchy and is used to obtain regulatory approval of new interventions.

Interventional confirmatory clinical trial with pre-specified interim adaptations

Patient-level longitudinal clinical outcome data (efficacy, safety, biomarkers)

International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2019). ICH E9(R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials. ICH Harmonised Guideline.

U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.

adaptive-randomized-clinical-trial phase-iii-clinical-trial randomized-clinical-trial adaptive-phase-ii-clinical-trial bayesian-phase-iii-clinical-trial multicenter-randomized-clinical-trial


Adaptive Phase IV study

adaptive post-marketing surveillance study

1990s–2000s (regulatory formalization of adaptive Phase IV designs)

Adaptive design principles developed by multiple statisticians; Phase IV framework established by regulatory bodies (FDA, EMA) in the late 20th century

An Adaptive Phase IV study is a post-marketing surveillance study conducted after a drug or intervention has received regulatory approval, augmented with pre-specified adaptive design elements that allow pre-planned modifications to the study protocol in response to accumulating data. These modifications may include sample size re-estimation, endpoint adjustments, or population enrichment, all governed by statistical rules set before the study begins, preserving scientific integrity while increasing efficiency.

Adaptive post-marketing clinical study design

Real-world longitudinal data, patient registries, electronic health records, adverse event reports

phase-iv-study adaptive-randomized-clinical-trial cohort-study adaptive-phase-iii-clinical-trial pragmatic-phase-iv-study bayesian-phase-iv-study


Adaptive Pretest-Posttest Experimental Design

adaptive pre-post design
adaptive pretest-posttest trial
adaptive two-period design
pre-post adaptive experiment

2000s (integration of adaptive principles with classic pre-post structure)

Synthesizes Campbell & Stanley (1963) pretest-posttest framework with adaptive design methodology formalized by Chow & Chang (2000s)

An adaptive pretest-posttest experimental design measures all participants before and after an intervention while allowing pre-specified modifications to the trial — such as sample size re-estimation, treatment arm dropping, or randomization ratio adjustment — based on accumulated interim data. It combines the interpretive power of change-score analysis with the efficiency gains and ethical safeguards of adaptive methodology, making it particularly valuable in clinical, educational, and behavioral research where early data can inform better resource allocation.

Continuous or categorical outcome measurements at two time points (pre and post)

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.


Adaptive Purposive Sampling

iterative purposive sampling
emergent purposive sampling
adaptive qualitative sampling
dynamic purposive sampling

Rooted in Patton's purposive sampling typology; adaptive dimension from iterative qualitative inquiry traditions

Adaptive purposive sampling is a qualitative strategy in which the researcher begins with explicitly stated, theory-driven selection criteria and then deliberately revises those criteria as data collection proceeds and new understanding emerges. Unlike fixed purposive sampling — where criteria are locked in before fieldwork — the adaptive variant treats the sampling frame as a working hypothesis that is refined in response to early findings, enabling the study to follow the evidence into unexpected but analytically important directions.

Qualitative data (interviews, observations, documents)

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.

Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13(6), 522–525.

purposive-sampling theoretical-sampling snowball-sampling maximum-variation-sampling adaptive-cluster-sampling deviant-case-sampling


Adaptive Quota Sampling

responsive quota sampling
dynamic quota sampling
iterative quota sampling

2000s (adaptive extension of quota principles)

Grounded in quota sampling (Quota sampling formalized early 20th century); adaptive extensions developed within responsive survey design frameworks (Groves & Heeringa, 2006)

Adaptive quota sampling is a non-probability sampling approach that starts with predefined demographic or characteristic-based quotas and then adjusts those quotas during data collection in response to emerging response patterns, nonresponse trends, or representativeness concerns. By treating the sampling process as iterative rather than fixed, it allows researchers to correct imbalances in real time and improve the final sample composition without restarting data collection from scratch.

Non-probability sampling with adaptive control

Survey data, structured questionnaire responses

Groves, R. M., & Heeringa, S. G. (2006). Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society: Series A, 169(3), 439–457.

Neyman, J. (1934). On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection. Journal of the Royal Statistical Society, 97(4), 558–625.

quota-sampling adaptive-stratified-sampling proportional-quota-sampling disproportional-quota-sampling stratified-sampling responsive-design


Adaptive Randomized Clinical Trial

adaptive RCT
adaptive trial design
response-adaptive randomization trial
adaptive clinical trial

Late 1990s–2000s (widespread adoption post-2010)

Donald Berry and colleagues; formalized by FDA guidance in 2010 and 2019

An adaptive randomized clinical trial (adaptive RCT) is a prospective experimental study that uses pre-specified rules to modify one or more trial aspects — such as sample size, allocation ratios, or treatment arms — based on accumulating data collected during the trial itself, while maintaining statistical validity and integrity of the study.

Accumulating patient outcome data (continuous monitoring)

Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.

randomized-clinical-trial phase-ii-clinical-trial phase-iii-clinical-trial bayesian-randomized-clinical-trial pragmatic-randomized-clinical-trial dose-response-analysis


Adaptive Randomized Controlled Trial

Adaptive RCT
Response-adaptive RCT
Adaptive clinical trial
Platform trial

1980s–2000s (formalized; earlier sequential testing roots from Wald, 1947)

Donald Berry and others; foundational adaptive trial methods developed through 1980s–2000s biostatistics literature

An adaptive randomized controlled trial (adaptive RCT) is an experimental design in which pre-specified rules allow modifications to the trial while it is ongoing — such as changing allocation ratios, dropping underperforming arms, or stopping early for efficacy or futility — based on accumulating interim data. These adaptations are planned before the trial starts and governed by statistical rules to preserve Type I error control and validity.

Experimental design — adaptive variant of RCT

Continuous, binary, or time-to-event outcome data collected during the trial

randomized-controlled-trial multi-arm-experiment adaptive-experiment bayesian-inference sequential-analysis factorial-randomized-controlled-trial


Adaptive Sampling

Adaptive Cluster Sampling
Sequential Adaptive Sampling
Network Sampling
Adaptif Küme Örneklemesi

Adaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena.

Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.


Adaptive screening test evaluation

adaptive screening
computerized adaptive screening
tailored screening test evaluation
CAT-based screening evaluation

1980s–1990s (formal adaptive screening frameworks)

Lord, F. M. (IRT foundations); Wainer & colleagues (CAT adaptation to screening)

Adaptive screening test evaluation is a psychometric and epidemiological framework for designing and assessing screening instruments whose item selection or stopping rules adjust dynamically to each respondent's response pattern. Rooted in item response theory (IRT) and computerized adaptive testing (CAT), the method uses real-time ability or severity estimates to present only the most informative items, then evaluates the resulting screening decisions against a clinical reference standard using standard diagnostic accuracy metrics.

Item-level dichotomous or polytomous responses; criterion diagnosis data

Wainer, H., Dorans, N. J., Flaugher, R., Green, B. F., & Mislevy, R. J. (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.

Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health Measurement Scales: A Practical Guide to Their Development and Use (5th ed.). Oxford University Press.


Adaptive Simple Random Sampling

ASRS
adaptive SRS
adaptive random sampling
sequential adaptive sampling

Adaptive simple random sampling (ASRS) begins with a conventional simple random sample and then expands the sample in regions where the variable of interest exceeds a pre-specified threshold. Units neighboring a qualifying observation are added to the sample, allowing the design to concentrate effort where the population is dense or rare, while retaining unbiased estimation through the Horvitz-Thompson or Hansen-Hurwitz estimators. The approach was systematized by Steven K. Thompson in the early 1990s as part of the broader adaptive sampling framework.

Probability-based adaptive sampling design

Quantitative (continuous or count data); population values observed during sampling

Thompson, S. K. (1992). Sampling. John Wiley & Sons.

Thompson, S. K., & Seber, G. A. F. (1996). Adaptive Sampling. John Wiley & Sons.

simple-random-sampling adaptive-cluster-sampling systematic-sampling stratified-sampling multistage-sampling sequential-sampling


Adaptive Single-Subject Experimental Design

Adaptive single-case experimental design

Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s

Evolved from classical single-case designs (Skinner, Sidman); adaptive features formalised in clinical N-of-1 literature (Zucker, Schmid, Nikles et al.)

Adaptive single-subject experimental design (adaptive SSED) is an experimental methodology in which a single participant or unit is repeatedly observed under systematically alternated conditions — baseline and intervention — while pre-specified decision rules allow the researcher or clinician to modify treatment parameters, phase lengths, or condition sequences in response to continuously collected data. It merges the internal validity of classical single-case experimental designs with the flexibility of adaptive trial logic, making it especially valuable in clinical, behavioral, and applied settings where individual response trajectories vary substantially.

Experimental single-subject design with adaptive decision rules

Repeated measures on a single participant or unit (behavioral counts, clinical scores, physiological measurements)

Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.

Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.

single-subject-experimental-design multiple-baseline-design reversal-design n-of-1-trial interrupted-time-series changing-criterion-design


Adaptive Snowball Sampling

adaptive referral sampling
adaptive chain-referral sampling
dynamic snowball sampling

Combines principles from S. K. Thompson (adaptive sampling, 1990) and L. A. Goodman (snowball sampling, 1961)

Adaptive snowball sampling is a hybrid sampling strategy that recruits initial participants (seeds) from a target population and then dynamically adjusts referral chains based on pre-specified criteria — such as population density, diversity, or theoretical saturation. Combining the chain-referral logic of snowball sampling with the responsive decision rules of adaptive sampling, it is particularly suited to studying rare, hidden, or hard-to-reach populations where conventional frames are unavailable.

Non-probability / adaptive sampling design

Social network data, participant referral chains, qualitative or quantitative data from hard-to-reach populations

Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32(1), 148–170.

snowball-sampling adaptive-cluster-sampling respondent-driven-sampling purposive-sampling theoretical-sampling network-sampling


Adaptive Solomon Four-Group Design

Adaptive Randomization Solomon Four-Group Experimental Design

adaptive S4G design
response-adaptive Solomon design
sequential Solomon four-group design
adaptive pretest-sensitization design

1949 (base design); adaptive adaptation developed through later adaptive trial methodology

Richard L. Solomon (base design); adaptive extension via response-adaptive randomization methodology

The Adaptive Solomon Four-Group Design combines the pretest-sensitization control of Solomon's classic four-group structure with response-adaptive randomization, allowing interim outcome data to update the allocation probabilities across the four groups as the study progresses. This hybrid preserves the design's ability to isolate the testing effect while improving ethical efficiency by steering more participants toward conditions performing better at interim checkpoints.

Experimental design (pretest-sensitization control + adaptive randomization)

Continuous or ordinal outcome measures; interim outcome data used to guide allocation

Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.

Hu, F., & Rosenberger, W. F. (2006). The Theory of Response-Adaptive Randomization in Clinical Trials. Wiley.

solomon-four-group-design adaptive-design crossover-solomon-four-group-design blocked-solomon-four-group-design randomized-controlled-trial factorial-experiment


Adaptive Stratified Sampling

ASS
adaptive stratified design
stratified adaptive sampling
adaptive allocation stratified sampling

1990s (formal development from Thompson 1990 onward)

Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others

Adaptive stratified sampling divides the population into strata and then applies an adaptive rule within each stratum: whenever an initially selected unit satisfies a pre-specified condition (e.g., a rare species is found, a variable exceeds a threshold), neighboring or related units are added to the sample. This combines the variance-reduction power of stratification with the ability to concentrate sampling effort where the phenomenon of interest is actually present.

Numerical counts, measurements, or categorical attributes from population units

Thompson, S. K. (2002). Sampling (2nd ed.). Wiley-Interscience.

stratified-sampling adaptive-cluster-sampling proportional-stratified-sampling disproportional-stratified-sampling multistage-sampling systematic-sampling


Adaptive Survival Analysis

adaptive time-to-event analysis
adaptive event-driven trial analysis
adaptive hazard modeling
ASA

Bauer, Posch, and collaborators (adaptive design framework); Lachin & Foulkes (event-driven survival trial foundations)

Adaptive survival analysis integrates adaptive clinical trial design with time-to-event statistical methods, allowing pre-specified modifications to sample size, event targets, or allocation ratios at interim stages based on accumulating survival data. It is widely used in oncology, cardiovascular, and infectious disease research where the primary endpoint is a hazard-based outcome such as progression-free survival or all-cause mortality.

Adaptive statistical design for time-to-event outcomes

Time-to-event (censored survival) data from clinical or epidemiological studies

Bauer, P., & Posch, M. (2004). Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections. Statistics in Medicine, 23(8), 1333–1353.

Mehta, C., Bhatt, M., & Bhattacharya, R. (2009). Adaptive randomization for survival endpoints in oncology trials. Journal of Clinical Oncology, 27(15_suppl), e20750.

kaplan-meier-estimator cox-proportional-hazards group-sequential-design interim-analysis log-rank-test competing-risks-analysis


Adaptive Trial Design

Adaptive Clinical Trial Design with Pre-Planned Interim Analyses

adaptive trial
adaptive design
response-adaptive randomization
RAR
seamless phase II/III

Stephen Pocock, Christopher Jennison, and statistical methodologists; FDA formalized guidance 2019

An adaptive trial design allows pre-specified modifications to the trial based on interim data—such as sample size re-estimation, stopping for futility or efficacy, dropping ineffective arms, or shifting randomization ratios toward better-performing treatments. Developed systematically in the 1990s–2000s by statisticians like Pocock and Jennison, and formalized by the FDA in 2019, adaptive designs accelerate drug development, reduce exposure to ineffective treatments, and improve efficiency without inflating false-positive rates when properly executed.

Pocock, S. J. (2005). Current issues in the design and interpretation of clinical trials. BMJ, 330(7500), 1118–1121.

Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., ... & Wason, J. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.

FDA (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. US Food and Drug Administration.

randomized-controlled-trial interim-analysis response-adaptive-randomization sequential-testing sample-size-reestimation


Adaptive Weighted Sampling

AWS
adaptive importance sampling
sequential adaptive weighting
dynamic weighted sampling

Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature

Adaptive weighted sampling is a probabilistic sampling procedure that assigns and iteratively updates inclusion weights for population units based on observed data collected during the sampling process itself. Unlike static weighted sampling — where weights are fixed before data collection from known auxiliary information — adaptive weighting revises probabilities as new information accumulates, concentrating sampling effort on units that contribute most to estimating the target quantity. It is used in survey methodology, simulation studies, and rare-event estimation.

Quantitative population data; numeric survey variables

Owen, A. B. (2000). Monte Carlo Theory, Methods and Examples. Stanford University (online edition). Chapter on importance sampling and adaptive weighting.

weighted-sampling adaptive-cluster-sampling stratified-sampling systematic-sampling importance-sampling multistage-sampling


Alzheimer's Disease Assessment Scale-Cognitive Subscale

The Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) is a clinician-administered cognitive assessment instrument designed specifically to measure cognitive decline in Alzheimer's disease. Developed by Rosen, Mohs, and Davis in 1984 and published in the American Journal of Psychiatry, the ADAS-Cog has become the gold standard outcome measure in pharmaceutical trials of antidementia drugs. It is sensitive to disease progression and capable of detecting cognitive change over periods as brief as 6–12 months.

Clinician-administered cognitive scale for Alzheimer's disease

Rosen, W. G., Mohs, R. C., & Davis, K. L. (1984). A new rating scale for Alzheimer's disease. American Journal of Psychiatry, 141(11), 1356-1364.

Mohs, R. C., Knopman, D., Petersen, R. C., et al. (1997). Development of cognitive instruments for use in clinical trials of antidementia drugs: Additions to the ADAS and MMSE. Alzheimer Disease and Associated Disorders, 11(Suppl 2), 13-21.

Pfeffer, R. I., Inoue, S. K., & Chance, G. R. (2000). Diagnostic criteria for dementia: Revision of the DSM-IV and ICD-10. Journal of the American Geriatrics Society, 48(12), 1572-1578.

mmse saint-louis-mental-status dementia-rating-scale addenbrookes-cognitive-examination frontal-assessment-battery


Addenbrooke's Cognitive Examination

ACE
ACE-R
ACE-III
Addenbrooke Cognitive Examination

The Addenbrooke's Cognitive Examination (ACE) is a brief yet comprehensive clinician-administered cognitive battery designed to assess multiple cognitive domains and differentiate between types of dementia. Originally developed by Mathuranath and colleagues at Cambridge University in 2000, the ACE was created to address limitations of single-domain screening tools. The revised version (ACE-R, 2006) and further refined version (ACE-III, 2013) provide updated norms and improved sensitivity. The ACE-R and ACE-III are particularly valuable for distinguishing Alzheimer's disease from frontotemporal dementia.

Clinician-administered comprehensive cognitive examination

Mathuranath, P. S., Nestor, P. J., Berrios, G. E., Rakowicz, W., & Hodges, J. R. (2000). A brief cognitive test battery to differentiate Alzheimer's disease and frontotemporal dementia. Neurology, 55(11), 1613-1620.

Mioshi, E., Dawson, K., Mitchell, J., Arnold, R., & Hodges, J. R. (2006). The Addenbrooke's Cognitive Examination Revised (ACE-R): A brief cognitive test battery for dementia screening. International Journal of Geriatric Psychiatry, 21(11), 1078-1085.

Hsieh, S., Schubert, S., Hoon, C., Mioshi, E., & Hodges, J. R. (2013). Validation of the Addenbrooke's Cognitive Examination III in frontotemporal dementia and Alzheimer's disease. Dementia and Geriatric Cognitive Disorders, 36(3-4), 242-250.

mmse adas-cog saint-louis-mental-status dementia-rating-scale frontal-assessment-battery


Addiction Severity Index

Comprehensive substance use disorder severity assessment

The ASI is a multidimensional, clinician-administered semi-structured interview assessing severity of substance use disorder and related psychosocial problems across seven domains: medical, employment, drug use, alcohol use, legal, family/social, and psychiatric. Developed by McLellan and colleagues in 1980 and refined through editions, it has become the gold standard comprehensive assessment tool in addiction medicine, substance abuse treatment programs, and research. The ASI provides both interview-derived severity ratings (0–9 per domain) and composite scores enabling treatment planning and outcome monitoring.

McLellan, A. T., Luborsky, L., Woody, G. E., & O'Brien, C. P. (1980). An improved diagnostic evaluation instrument for substance abuse patients: The Addiction Severity Index. Journal of Nervous and Mental Disease, 168(1), 26–33.

McLellan, A. T., Kusama, H. F., & Metzger, D. S. (1992). The fifth edition of the Addiction Severity Index. Journal of Substance Abuse Treatment, 9(3), 199–213.

Cacciola, J. S., Alterman, A. I., McLellan, A. T., Lin, Z. B., & Lynch, K. G. (1997). Initial evidence for the reliability and validity of a "lite" version of the Addiction Severity Index. Drug and Alcohol Dependence, 44(1), 9–19.


Additive Manufacturing Slicing

Additive Manufacturing Slicing and Layer Generation

3D printing slicing
Layer generation
Mesh slicing

Additive manufacturing slicing is the computational process of converting a three-dimensional CAD model into a series of two-dimensional cross-sectional layers that are sequentially built up by 3D printing hardware. Developed during the early maturation of stereolithography and selective laser sintering in the 1990s, this method bridges the gap between digital design and physical fabrication, enabling rapid prototyping and production of complex geometries.

Computational method for additive manufacturing

Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T., & Hui, D. (2018). Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites Part B: Engineering, 143, 172-196.

Gibson, I., Rosen, D. W., & Stucker, B. (2015). Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. Springer-Verlag, 2nd edition.

Cheng, B., Chou, K., & Hsu, K. (2019). Experimental and numerical investigation on deformation and cracking of aluminum alloy cubes during direct laser additive manufacturing. Journal of Manufacturing Processes, 41, 131-143.

cnc-tool-path-generation design-for-manufacturing-and-assembly tolerance-stack-up modal-analysis


Augmented Dickey-Fuller Test

Augmented Dickey-Fuller (ADF) Unit-Root Test

ADF test
Dickey-Fuller test
unit root test
Genişletilmiş Dickey-Fuller testi

The Augmented Dickey-Fuller (ADF) test is the most widely used test for a unit root — that is, for whether a time series is non-stationary and must be differenced before modelling. Introduced by David Dickey and Wayne Fuller in 1979 and extended by Said and Dickey in 1984 to series with higher-order autocorrelation, it regresses the change in the series on its lagged level plus lagged differences and asks whether the lagged-level coefficient is zero.

Series contains a unit root (non-stationary)

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431.

Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607.

kpss-test phillips-perron-test cointegration-test arima vecm


Adjusted Boxplot

Düzeltilmiş Kutu Grafiği (Adjusted Boxplot)

The Adjusted Boxplot is a robust descriptive tool introduced by Hubert and Vandervieren (2008) that corrects the classical IQR-based boxplot for skewness using the medcouple statistic, reducing the false labelling of outliers in asymmetric data.

Robust outlier detection / descriptive visualization

Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.

mad-estimation sn-qn-estimators jackknife robust-time-series bootstrap-inference


Adjusted R-squared

Adjusted R² is a corrected version of the coefficient of determination that accounts for the number of predictors in a regression model. Introduced by Henri Theil in 1961, it addresses the fundamental limitation of standard R²: the tendency to increase whenever any predictor is added, regardless of whether that predictor contributes meaningfully to explaining the target variable.

Theil, H. (1961). Economic Forecasts and Policy. Amsterdam: North-Holland Publishing Company.

Ezekiel, M. (1930). Methods of Correlation Analysis. New York: John Wiley & Sons.

Judge, G. G., Griffiths, W. E., Hill, R. C., Lütkepohl, H., & Lee, T. C. (1985). The Theory and Practice of Econometrics. New York: John Wiley & Sons.

r-squared akaike-information-criterion bayesian-information-criterion mean-squared-error root-mean-squared-error


Adjusted Rand Index

Adjusted Rand Index for External Cluster Evaluation

The Adjusted Rand Index (ARI), developed by Hubert and Arabie in 1985, is an external clustering evaluation metric that measures the agreement between a predicted clustering and a ground truth labeling. It ranges from -1 to 1, where 1 indicates perfect agreement, 0 indicates random clustering, and negative values indicate performance worse than random chance.

Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218.

Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336), 846-850.

normalized-mutual-information fowlkes-mallows-index v-measure silhouette-score davies-bouldin-index


Admixture Analysis

Population Admixture Analysis and Ancestry Inference

Population structure inference
Ancestry analysis
ADMIXTURE

Admixture analysis is a population genetics method that infers population structure and individual ancestry from multilocus genotype data. Originally developed by Pritchard, Stephens, and Donnelly (2000) and refined by Alexander, Novembre, and Lange (2009), admixture analysis reveals how genetic variation is distributed among populations and estimates the ancestry fractions of admixed individuals. This technique is essential for understanding human evolutionary history, detecting population stratification in genetic studies, and inferring individual ancestry.

Alexander, D. H., Novembre, J., & Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655–1664.

Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure from multilocus genotype data. Genetics, 155(2), 945–959.

Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A., & Feldman, M. W. (2002). Genetic structure of human populations. Science, 298(5602), 2381–2385.

f-statistics ld-block-analysis coalescent-theory phylogenetic-independent-contrasts


AddiQoL

Adrenal Insufficiency Quality of Life Scale

Pituitary/adrenal hormone deficiency quality of life

AddiQoL is a disease-specific quality of life questionnaire developed to assess the burden of primary and secondary adrenal insufficiency, encompassing physical, emotional, and social domains relevant to patients on glucocorticoid and mineralocorticoid replacement therapy. Developed by Evers and Tiemensma in 2012, it is the first formally validated instrument specifically designed for adrenal insufficiency populations.

Evers, A. C., & Tiemensma, J. (2012). AddiQoL: A disease-specific quality of life scale for patients with primary adrenal insufficiency. J Clin Endocrinol Metab, 97(10), 3501-3508.

Tiemensma, J., & Evers, A. C. (2014). Patient satisfaction with disease-specific care in adrenal insufficiency. Endocr Pract, 20(6), 551-558.


Adsorption Isotherm Models (Langmuir, Freundlich, and Combined)

Langmuir isotherm
Freundlich isotherm
sorption equilibrium

Adsorption isotherms describe the equilibrium uptake of a substance on a solid surface as a function of gas or solution phase concentration at constant temperature. The Langmuir isotherm (1918) and Freundlich isotherm (1906) are classical empirical models. The Langmuir model assumes monolayer coverage and is mechanistic; the Freundlich model is empirical and describes multilayer or heterogeneous adsorption. These isotherms are essential for designing separation processes (activated carbon filters, molecular sieves) and understanding pollutant sorption.

Empirical and theoretical adsorption equilibrium model

Langmuir, I. (1918). The adsorption of gases on plane surfaces of glass, mica, and platinum. Journal of the American Chemical Society, 40(9), 1361-1403.

Freundlich, H. M. F. (1906). Über die Adsorption in Lösungen. Zeitschrift für Physikalische Chemie, 57(1), 385-470.

Yang, R. T. (1997). Gas Separation by Adsorption Processes. Butterworth-Heinemann.


Adult ADHD Self-Report Scale

Adult ADHD Self-Report Scale (ASRS-v1.1)

The ASRS-v1.1 is an 18-item self-report screening scale for attention-deficit/hyperactivity disorder in adults, developed by Kessler and colleagues in 2005 under World Health Organization auspices. A brief 6-item version provides rapid initial screening. The scale has become standard first-step screening in primary care, occupational medicine, and mental health settings, particularly valuable for identifying undiagnosed ADHD in working-age adults.

Kessler, R. C., Adler, L., Ames, M., et al. (2005). The World Health Organization Adult ADHD Self-Report Scale (ASRS): a short screening scale and symptom impact measure. Psychological Medicine, 35(2), 245–256.


AAG

The Adult Attitude to Grief Scale (AAG) is a measure assessing individual beliefs, attitudes, and values regarding grief and bereavement. Developed by Richard K. Barrett, the AAG captures how adults conceptualize grief—including beliefs about whether grief is acceptable, whether emotions should be expressed, whether seeking help is appropriate, and whether personal growth can emerge from loss. By measuring grief-related attitudes, the AAG provides insight into psychological readiness for adaptive bereavement.

Barrett, R. K. (1994). Conceptualizing adult grief. American Behavioral Scientist, 46(2), 263–276.

grief-experience-questionnaire texas-revised-inventory-grief inventory-complicated-grief anticipatory-grief-scale


Adversarial Training

Adversarial Training (Robust Optimization for DL)

Min-Max Robust Training
PGD Adversarial Training
Robust Empirical Risk Minimization
Hasımsal Eğitim

Adversarial Training is a robust optimization procedure for deep neural networks in which the model is trained not on clean data alone but on worst-case perturbed inputs crafted during training. Formalized by Madry et al. (2018) as a min-max saddle-point problem, the method uses Projected Gradient Descent (PGD) to generate strong adversarial examples within a bounded Lp perturbation set before each gradient update, forcing the network to learn decision boundaries that are stable under such perturbations.

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. International Conference on Learning Representations (ICLR).


Advertising Effectiveness Study

Advertising Effectiveness Study and Impact Measurement

Ad Effectiveness Testing
Campaign Evaluation
Marketing Attribution

Media measurement and campaign evaluation

Marketing Science Institute and Media Effectiveness researchers

Advertising Effectiveness Studies are research methods designed to measure the impact of advertising campaigns on consumer awareness, attitudes, purchase intention, and sales. Developed through work in marketing science and media measurement, these studies employ experimental designs, multivariate analysis, and attribution modeling to isolate the effect of advertising from other market factors.

Experimental and observational evaluation methodology

Erdem, T., & Sun, B. (2002). A Fuzzy Aspect Model for CRM System Selection. Decision Support Systems, 29(3), 475-487.

Hanssens, D. M., Parsons, L. J., & Schultz, R. L. (2001). Market Response Models: Econometric and Time Series Analyses (2nd ed.). Kluwer Academic Publishers.

Campbell, M. C., & Kirmani, A. (2000). Consumers' Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent. Journal of Consumer Research, 27(1), 69-83.

marketing-mix-modeling brand-equity-measurement customer-journey-mapping net-promoter-score market-segmentation-analysis


Aerosol Optical Depth

Aerosol Optical Depth (AOD) is a dimensionless measure of aerosol light extinction in the atmosphere, quantifying how much sunlight is scattered and absorbed by particles suspended in air. Formalized by Ångström in 1929 and now routinely measured via satellite (MODIS, Sentinel-5P) and ground networks (AERONET), AOD is essential for air quality monitoring, climate forcing assessment, and visibility prediction.

Optical parameter for aerosol loading quantification

Ångström, A. (1929). On the atmospheric transmission of sun radiation and on dust in the air. Geografiska Annaler, 11(2), 156-166.

Holben, B. N., et al. (1998). AERONET: A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1), 1-16.


AES (Rijndael)

The Advanced Encryption Standard (AES), also known as Rijndael, is a symmetric block cipher adopted as the official encryption standard by the U.S. government in 2001. It processes data in 128-bit blocks using 128, 192, or 256-bit keys and performs multiple rounds of substitution, permutation, and mixing operations. AES is the most widely used symmetric encryption algorithm today, securing everything from government communications to everyday internet traffic.

Daemen, J., & Rijmen, V. (2002). The Design of Rijndael: AES - The Advanced Encryption Standard. Springer-Verlag.

National Institute of Standards and Technology (NIST). (2001). FIPS 197: Specification for the Advanced Encryption Standard (AES). U.S. Department of Commerce.

rsa-cryptosystem hmac differential-cryptanalysis linear-cryptanalysis


Affective Lability Scale

Philip D. Harvey, Bruce R. Greenberg, Maurizio R. Serper

The ALS is a 54-item self-report measure of affective lability—rapid, unpredictable shifts in mood and anxiety states. Developed by Harvey, Greenberg, and Serper in 1989, it distinguishes normal emotional responsiveness from pathological mood instability. Affective lability is recognized as feature of bipolar disorder, borderline personality disorder, certain anxiety disorders, and represents dimensional measure of emotion dysregulation.

Harvey, P. D., Greenberg, B. R., & Serper, M. R. (1989). The affective lability scales: Development, reliability, and validity. Journal of Clinical Psychology, 45(6), 786–793.


Affinity Propagation

affinity propagation clustering
message-passing clustering
exemplar-based clustering
yakınlık yayılımı kümeleme

Affinity propagation, introduced by Brendan Frey and Delbert Dueck in 2007, is a clustering algorithm that identifies representative 'exemplars' among the data by exchanging messages between every pair of points until a consistent set of clusters emerges. Unlike k-means it does not require the number of clusters to be specified in advance — that number arises from the data and a 'preference' parameter — and it works directly from pairwise similarities, which need not be a metric.

Exemplar-based clustering via message passing

Input: Pairwise similarities (need not be metric)

Preference (controls number of exemplars)

Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976.

k-means-clustering dbscan spectral-clustering hierarchical-clustering


African Vultures Optimization Algorithm

The African Vultures Optimization Algorithm (AVOA) is a metaheuristic algorithm introduced by Moghdani and Salimifard in 2020, inspired by the search and scavenging behavior of African vultures. Vultures employ sophisticated collaborative strategies to locate carrion across vast distances, using thermal air currents and group dynamics to navigate efficiently. AVOA translates these collective hunting behaviors into an effective optimization framework.

Moghdani, H., & Salimifard, K. (2020). Volleyball player optimizer and African vultures optimization algorithms for solving global optimization problems. Applied Soft Computing, 97, 106794.

harris-hawks-optimization aquila-optimizer slime-mould-algorithm particle-swarm-optimization eagle-strategy


Agenda-Setting Analysis

Media Agenda-Setting and Issue Salience Analysis

agenda-setting theory
media agenda analysis
issue salience

Media effects and public opinion research

Agenda-Setting Analysis is an empirical method for investigating the influence of media coverage on what issues the public considers important. Developed by Maxwell McCombs and Donald Shaw (1972), the approach tests a core hypothesis about media effects: media coverage does not tell people what to think, but rather what to think about. By comparing the issues receiving media coverage with the issues the public identifies as important, researchers measure agenda-setting effects—the degree to which media attention predicts public concern. The method demonstrates media's power to structure the hierarchy of issues, even when media may not directly persuade on specific issues.

Empirical method for studying how media coverage affects issue salience and public concern

McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176-187.

Weaver, D. H. (1997). Media agenda setting in the presidential election. In M. E. McCombs, D. L. Shaw, & D. H. Weaver (Eds.), Communication and Democracy (pp. 15-32). Lawrence Erlbaum.

McCombs, M. E. (2014). Setting the Agenda: The News Media and Public Opinion (2nd ed.). Polity Press.

Soroka, S. N. (2012). The gatekeepers: The media, the public, and policy-making. Canadian Public Policy, 38(4), 459-474.

media-framing-analysis discourse-analysis-media reception-analysis visual-content-analysis film-narrative-analysis


Agent-based ant colony optimization

AB-ACO
Agent-Based ACO
Multi-Agent Ant Colony Optimization
MAACO

Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community

Agent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on a search graph. By coupling agent-level behavioral rules with a shared pheromone environment, the collective system converges on high-quality solutions to hard combinatorial and simulation-embedded optimization problems without central coordination.

Metaheuristic optimization — agent-based swarm simulation

Combinatorial or continuous optimization problem instances; graph/network structures

Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA.

Bonabeau, E., Dorigo, M., Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York.

ant-colony-optimization agent-based-modeling multi-objective-ant-colony-optimization stochastic-ant-colony-optimization particle-swarm-optimization genetic-algorithm


Agent-based cellular automata

Agent-Based Cellular Automata Simulation

ABCA
CA-ABM
Agent-CA
Hybrid Agent-Cellular Automaton

Wolfram, S.; Epstein, J. M. & Axtell, R.

Agent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-based modeling. Cells in a spatial grid both evolve according to neighborhood rules and host agents that perceive, decide, and act, enabling the study of complex spatial phenomena such as land-use change, disease spread, crowd dynamics, and ecosystem evolution.

Spatial grid data, agent state variables, environmental attributes

Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL.

Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. Brookings Institution Press / MIT Press, Washington, DC.

agent-based-modeling cellular-automata agent-based-system-dynamics stochastic-cellular-automata discrete-event-simulation multi-objective-cellular-automata


Agent-based Discrete-Event Simulation

Agent-Based Discrete-Event Simulation (AB-DES)

Hybrid Agent-Based Discrete-Event Simulation

Hybridization formalized by multiple authors; Siebers & Aickelin, Lagergren & Buckley among key contributors

Agent-based discrete-event simulation (AB-DES) is a hybrid modeling paradigm that couples autonomous agent behavior with an event-driven execution engine. It captures the decision-making heterogeneity of individual entities while maintaining the precise, time-stamped flow control of discrete-event simulation, making it suitable for complex systems where both individual agency and process sequencing matter.

Event logs, agent state data, process flow data, timestamped records

Lagergren, J. H., & Buckley, E. (2010). A hybrid approach to simulation: Combining agent-based and discrete event simulation. Proceedings of the 2010 Winter Simulation Conference, pp. 170–181. IEEE.

agent-based-modeling discrete-event-simulation stochastic-agent-based-modeling agent-based-system-dynamics monte-carlo-simulation multi-method-simulation


Agent-based dynamic programming

Agent-Based Dynamic Programming — Sequential Decision-Making in Multi-Agent Systems

ABDP
Agent-based DP
Multi-agent dynamic programming
ABM-DP

1957 (DP); 1990s onward (ABM integration)

Bellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration)

Agent-based dynamic programming (ABDP) embeds Bellman's dynamic programming framework within individual agents of an agent-based model, enabling each agent to solve sequential, multi-stage decision problems using backward induction or value-function iteration. The result is a population of optimizing agents whose interactions generate emergent system-level behavior.

Agent state spaces, transition functions, reward/payoff data

Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.

Tesfatsion, L., Judd, K. L. (Eds.) (2006). Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. Elsevier, Amsterdam.

dynamic-programming agent-based-modeling markov-decision-process reinforcement-learning stochastic-dynamic-programming multi-objective-dynamic-programming


Agent-based genetic algorithm

ABGA
Agent-Based GA
Multi-Agent Genetic Algorithm
Distributed Agent GA

Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s

An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration of the search space while preserving population diversity and avoiding premature convergence.

Objective function evaluations, agent state data, population fitness vectors

Adamidis, P., & Petridis, V. (1996). Co-operating populations with different evolution behaviors. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1996), 188-191. IEEE.


Agent-based goal programming

Agent-Based Goal Programming — Hybrid simulation-optimization with decentralized agents and multi-goal satisfaction

ABGP
Agent-Based GP
ABM-GP
Agent-Driven Goal Programming

Charnes, Cooper (GP); Schelling, Holland (ABM foundations)

Agent-Based Goal Programming (ABGP) integrates agent-based simulation with goal programming optimization to model systems where multiple autonomous decision-makers pursue competing, prioritized goals. It enables researchers to study how decentralized, adaptive behavior at the agent level leads to system-level outcomes measured against predefined targets, capturing both emergence and multi-criteria satisfaction simultaneously.

Agent behavioral rules, goal targets, deviation weights

Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138-151.

Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162.


Agent-based integer programming

Agent-Based Integer Programming — Hybrid optimization integrating agent-based modeling with integer programming

ABIP
Agent-based IP
Multi-agent integer programming
ABM-IP

Emerged from multi-agent systems and operations research communities

Agent-Based Integer Programming (ABIP) couples the behavioral richness of agent-based modeling with the combinatorial rigor of integer programming. Individual agents pursue local objectives while a global IP solver enforces discrete feasibility constraints, enabling realistic modeling of multi-actor systems where decisions must be integer-valued — such as resource allocation, scheduling, and network design under emergent interaction effects.

Discrete decision variables, agent state data, constraint parameters

Wooldridge, M. (2009). An Introduction to MultiAgent Systems (2nd ed.). Wiley.

agent-based-modeling integer-programming agent-based-linear-programming agent-based-mixed-integer-programming multi-objective-integer-programming stochastic-integer-programming


Agent-based Markov model

Agent-Based Markov Model — Hybrid simulation combining autonomous agents with Markov chain state transitions

ABMM
Agent-Based Markov Chain Model
ABM-Markov hybrid
Agent Markov simulation

Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature

The Agent-Based Markov Model (ABMM) is a hybrid simulation framework that embeds Markov chain state-transition logic inside individual autonomous agents. Each agent independently samples its next state from a probability transition matrix, enabling the model to capture both micro-level heterogeneity across agents and the tractable probabilistic structure of Markov chains. The approach is widely used in health economics, epidemiology, social science, and operations research.

Hybrid simulation — agent-based modeling with Markov state transitions

Agent attributes, state transition probability matrices, discrete or continuous time

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl 3), 7280-7287.

Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge, UK.

agent-based-modeling markov-model stochastic-markov-model discrete-event-simulation agent-based-discrete-event-simulation hidden-markov-model


Agent-based microsimulation

Microsimulation with Agent-Based Modeling

1957 (microsimulation); 2000s (hybrid ABMS)

Orcutt, G. H. (microsimulation roots); Bonabeau, E. and others (ABM integration)

Agent-based microsimulation (ABMS) merges traditional microsimulation's individual-level statistical tracking with agent-based modeling's behavioral rules and interaction mechanisms. It creates virtual populations of heterogeneous agents who evolve over time according to transition probabilities, adaptive behaviors, and social interactions, producing emergent system-level outcomes from micro-level dynamics.

Individual-level demographic, behavioral, or socioeconomic records

Birkin, M., & Clarke, M. (2012). The enhancement of spatial microsimulation models using geodemographics. Annals of Regional Science, 49(2), 515–532.

Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123.

agent-based-modeling microsimulation discrete-event-simulation system-dynamics stochastic-microsimulation multi-agent-simulation


Agent-Based Modeling

ABM
Ajan Tabanlı Modelleme (ABM)
multi-agent simulation
individual-based modeling

Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)

Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone.

No empirical sample required; agents are defined by rules

Social science, epidemiology, economics, ecology, healthcare

Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.

Wilensky, U. & Rand, W. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press.

system-dynamics discrete-event-simulation monte-carlo-simulation markov-chain-monte-carlo latin-hypercube-sampling


Agent-Based Multi-Objective Optimization — Decentralized evolutionary search across competing objectives

ABMOO
agent-driven MOO
multi-objective ABM optimization
ABMO

Bonabeau, Dorigo, Theraulaz; Coello Coello et al.

Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adaptive systems where objectives emerge from micro-level interactions rather than closed-form equations.

Agent rules, objective function definitions, constraint sets

Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press.

Coello Coello, C. A., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (2nd ed.). Springer.


Agent-based NSGA-II

Agent-Based Non-dominated Sorting Genetic Algorithm II — Simulation-Driven Evolutionary Multi-Objective Optimization

AB-NSGA-II
ABM-NSGA2
agent-driven NSGA-II
simulation-based NSGA-II

Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s

Agent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running a full agent simulation rather than by evaluating a closed-form function. This coupling enables multi-objective optimization over systems whose performance emerges from the micro-level interactions of autonomous agents rather than from analytically tractable equations.

Simulation-embedded evolutionary multi-objective optimizer

Agent behavioral rules, decision-variable encodings, multiple objective functions evaluated via simulation

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.


Agent-based queueing simulation

AB-QS
Agent-Based Queue Simulation
ABM Queueing
Agent Queue Simulation

Macal, C. M. & North, M. J. (hybrid formalization); queueing theory rooted in Erlang (1909)

Agent-Based Queueing Simulation (AB-QS) combines agent-based modeling with queueing theory to simulate systems where autonomous, decision-making entities interact through waiting lines and service points. Each entity (patient, customer, job) is modeled as an independent agent with its own state and behavioral rules, enabling richer, more realistic dynamics than classical queueing models alone.

Hybrid simulation — agent-based + queueing

Event logs, arrival/service time distributions, agent behavioral rules

Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.

agent-based-modeling queueing-simulation discrete-event-simulation agent-based-discrete-event-simulation stochastic-agent-based-modeling monte-carlo-simulation


Agent-based scenario analysis

ABSA
ABM scenario analysis
agent-based scenario planning
scenario-driven ABM

Axelrod, R.; Schoemaker, P. J. H. (combined lineage)

Agent-based scenario analysis embeds agent-based simulation models inside a structured scenario planning framework. Researchers define two to four contrasting future scenarios, configure agent populations and environmental rules to reflect each scenario's assumptions, run the simulation under each condition, and compare emergent outcomes. This makes it possible to explore how decentralized individual behaviors aggregate into system-level consequences under radically different futures.

Agent rules, behavioral parameters, qualitative scenario narratives

Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. Princeton, NJ.

Schoemaker, P. J. H. (1995). Scenario planning: A tool for strategic thinking. Sloan Management Review, 36(2), 25–40.

agent-based-modeling scenario-analysis system-dynamics monte-carlo-simulation policy-scenario-agent-based-modeling stochastic-agent-based-modeling


Agent-based sensitivity analysis

ABM sensitivity analysis
ABSA
SA for ABMs
agent-based model sensitivity testing

Adapted from global sensitivity analysis (Saltelli et al.) for agent-based models

Agent-based sensitivity analysis (ABSA) applies sensitivity analysis techniques to agent-based models (ABMs) to determine which input parameters most strongly influence emergent outputs. Because ABMs are stochastic and nonlinear, standard analytical derivatives are unavailable; ABSA uses designed simulation experiments — screening methods, variance-based indices, or regression-based surrogates — to rank parameter importance and guide model calibration and validation.

Agent-based model outputs (stochastic, emergent)

Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons.

ten Broeke, G., van Voorn, G., & Ligtenberg, A. (2016). Which Sensitivity Analysis Method Should I Use for My Agent-Based Model? Journal of Artificial Societies and Social Simulation, 19(1), 5.

monte-carlo-simulation variance-based-sensitivity-analysis one-at-a-time-sensitivity-analysis latin-hypercube-sampling agent-based-modeling morris-screening


Agent-based system dynamics

Agent-Based System Dynamics — Hybrid Simulation Combining Micro-Level Agents with Macro-Level Stock-and-Flow Structures

AB-SD
Hybrid ABM-SD
Agent-based SD
Multi-level hybrid simulation

Borshchev, A. & Filippov, A. (hybrid formalization); Sterman, J. D. (system dynamics foundation)

Agent-based system dynamics (AB-SD) is a hybrid simulation paradigm that couples agent-based modeling (ABM) at the micro level with system dynamics (SD) stock-and-flow structures at the macro level. This allows researchers to capture emergent individual behavior and feedback-driven aggregate dynamics within a single coherent model, making it especially valuable for complex socio-economic and epidemiological systems.

Time-series, stock-flow variables, agent state variables, population data

Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In Proceedings of the 22nd International Conference of the System Dynamics Society. Oxford, UK.

Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill, Boston.

agent-based-modeling system-dynamics stochastic-agent-based-modeling discrete-event-simulation agent-based-discrete-event-simulation multi-objective-system-dynamics


Agent-based Tabu Search

Agent-Based Tabu Search — Distributed Multi-Agent Metaheuristic Optimization

ABTS
Multi-Agent Tabu Search
Distributed Tabu Search
Cooperative Tabu Search

Glover, F. (tabu search); multi-agent extension by various researchers in the 1990s–2000s

Agent-Based Tabu Search (ABTS) embeds the tabu search metaheuristic inside a multi-agent framework where autonomous agents each run independent or cooperating tabu search threads, sharing promising solutions to escape local optima and collectively explore large combinatorial or continuous search spaces more effectively than a single-thread implementation.

Hybrid metaheuristic — agent-based distributed tabu search

Combinatorial or continuous optimization problem instances

Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.

Verhoeven, M. G. A., Aarts, E. H. L. (1995). Parallel local search. Journal of Heuristics, 1(1), 43–65.

tabu-search agent-based-modeling distributed-optimization multi-objective-tabu-search agent-based-genetic-algorithm cooperative-search


Ages and Stages Questionnaire: Social-Emotional, Second Edition

The ASQ:SE-2 is a parent-completed screening questionnaire assessing social-emotional competencies and behavioral concerns in infants and young children aged 3 months to 5.5 years. Developed by Squires, Bricker, and Twombly (2009) and revised in 2015, it measures domains including self-regulation, compliance, adaptive behaviors, autonomy, affect, and social interaction. The ASQ:SE-2 is widely used in pediatric primary care, early intervention, child care settings, and public health programs for routine developmental screening and identification of children at risk for social-emotional or behavioral difficulties.

Squires, J., Bricker, D., & Twombly, E. (2015). Ages and Stages Questionnaires: Social-Emotional (ASQ:SE-2): A Parent-Completed Child Monitoring System for Social-Emotional Behaviors. Paul H. Brookes Publishing.

Squires, J., Bricker, D., & Twombly, E. (2009). Ages and Stages Questionnaires: Social-Emotional (ASQ:SE): A Parent-Completed, Child-Monitoring System for Social-Emotional Behaviors. Research and Practice for Persons with Severe Disabilities, 34(3-4), 42-55.


Ages and Stages Questionnaire

The Ages and Stages Questionnaire (ASQ-3), third edition, developed by Jane Squires and Diane Bricker in 2009, is a parent-completed developmental screening tool designed to identify children aged 1 month to 5.5 years at risk for developmental delay. It is brief, economical, and well-suited for population screening in primary care, early intervention programs, and community settings.

Parent-report developmental screening questionnaire

Squires, J., & Bricker, D. (2009). Ages & Stages Questionnaires (ASQ-3): A parent-completed child monitoring system (3rd ed.). Paul H. Brookes Publishing.

Squires, J., Bricker, D., & Twombly, E. (2018). Ages & Stages Questionnaires: Social-Emotional (ASQ-SE-2): A parent-completed assessment tool. Journal of Developmental & Behavioral Pediatrics, 39(8), 635-643.

bayley-scales cbcl-child-behavior strengths-difficulties-questionnaire vanderbilt-adhd-scale


Aggregate Planning

sales and operations planning
production planning

Aggregate Planning (or Sales & Operations Planning, S&OP) is a collaborative, iterative process that balances demand and supply at a high level—typically grouping products into families and planning over a 3–18 month horizon. Developed formally by Tom Wallace and popularized through APICS, aggregate planning helps organizations align sales forecasts, production capacity, inventory, and workforce to meet demand efficiently while managing costs. It serves as the bridge between strategic business plans and detailed operational execution.

Wallace, T. F. (1992). Sales & Operations Planning: The how-to handbook. Cincinnati: APICS Publications.

Monk, E., & Wagner, B. (2006). Concepts in enterprise resource planning (2nd ed.). Boston: Course Technology.

material-requirements-planning scor-model bullwhip-effect inventory-routing vendor-managed-inventory


Agile Velocity Tracking

Velocity Measurement and Sprint Performance Analysis

sprint velocity
team capacity planning
burndown analysis

Velocity tracking measures the amount of work (typically story points or tasks) a team completes in a sprint, enabling capacity planning, release forecasting, and identification of process improvements. Introduced in Scrum methodology by Schwaber (2002), velocity provides empirical data for realistic sprint planning and project timeline prediction. Teams use velocity trends to identify bottlenecks and validate process improvements.

Schwaber, K., & Beedle, M. (2002). Agile Software Development with Scrum. Prentice Hall.

Cohn, M. (2005). Agile Estimating and Planning. Prentice Hall PTR.

McCartney, P., & Hough, R. (2008). Velocity tracking in agile software development. Journal of Software Engineering Research and Development, 1(2), 25–40.

technical-debt-measurement code-coverage-analysis software-complexity-metrics defect-prediction-model


Agoraphobia Cognitions Questionnaire

The Agoraphobia Cognitions Questionnaire (ACQ) is a 14-item self-report instrument that assesses catastrophic and safety-related thoughts in individuals with agoraphobia and panic disorder. Developed by Chambless and colleagues in 1984, it measures two domains: fear of loss of control and worry about social consequences. The ACQ is a cornerstone measure in clinical research and practice for understanding the cognitive mechanisms that maintain agoraphobic avoidance and panic-related anxiety.

Chambless, D. L., Caputo, G. C., Bright, P., & Gallagher, R. (1984). Assessment of fear in agoraphobics: The Body Sensations Questionnaire and the Agoraphobia Cognitions Questionnaire. Journal of Consulting and Clinical Psychology, 52(6), 1090–1097.


Agrometeorological Yield Model

Agrometeorological Crop Yield Prediction Model

crop yield model
agroclimatic yield model
weather-based yield model
meteorological crop model

1960s–1980s (systematic development; FAO frameworks 1979)

Multiple contributors (FAO, USDA, Wageningen University researchers)

An agrometeorological yield model is a quantitative framework that relates observed or forecasted weather variables — temperature, precipitation, solar radiation, humidity — to the final grain or biomass yield of a crop. Grounded in plant physiology and agricultural climatology, the approach is used worldwide in food security monitoring, insurance underwriting, irrigation planning, and climate-change impact assessment.

Meteorological observations, crop phenology records, soil data, remote-sensing indices

Doorenbos, J., & Kassam, A. H. (1979). Yield Response to Water. FAO Irrigation and Drainage Paper No. 33. Food and Agriculture Organization of the United Nations, Rome.

Lobell, D. B., & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11), 1443-1452.

crop-simulation-model remote-sensing-vegetation-index soil-water-balance-model regression-analysis time-series-analysis climate-impact-assessment


AHP-BOCR

Analytical Hierarchy Process with Benefits, Opportunities, Costs, and Risks (AHP-BOCR)

Thomas L. Saaty (AHP); BOCR framework development extended AHP

AHP-BOCR is an extension of the Analytic Hierarchy Process that incorporates strategic perspectives through the BOCR framework: Benefits, Opportunities, Costs, and Risks. Instead of optimizing a single objective, AHP-BOCR decomposes decisions into four strategic dimensions and uses a formula (Benefits × Opportunities) / (Costs × Risks) to synthesize a strategic priority. This approach is particularly suited to long-term, complex decisions with multiple stakeholder perspectives.

Hierarchical pairwise comparison with strategic perspective

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98.

Subagyo, B., & Tjahyono, T. (2010). Multiple criteria decision making in selecting strategic decisions. Journal of Organizational Computing and Electronic Commerce, 20(2), 200-216.


AHP

AHP (Analytic Hierarchy Process) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York

ahpsort aploco aras aroman artasi cobra cocoso codas


AHPSORT

AHPSORT (AHPSort — AHP-based classification of alternatives into ordered categories) is a sorting multi-criteria decision-making (MCDM) method introduced by Ishizaka, A., Nemery, P., Pearman, C. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

AHP-based sorting with limiting or central profiles

Ishizaka, A., Nemery, P., Pearman, C. (2012). AHPSort: An AHP-based method for sorting problems. International Journal of Production Research

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


AHRS

AHRS system
attitude reference
heading sensor

An Attitude Heading Reference System (AHRS) is a complete inertial navigation subsystem that estimates and outputs the three-dimensional orientation (attitude) and heading of a vehicle or platform. AHRS combines measurements from accelerometers, gyroscopes, and often magnetometers through sensor fusion algorithms (typically Kalman filters or complementary filters) to provide a drift-free, fast attitude estimate. AHRS is standard in aviation, marine navigation, and modern autonomous systems.

Savage, P. G. (2007). Strapdown Inertial Integration Technology (2nd ed.). Strapdown Associates.

Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.

Madgwick, S. O. H., Harrison, A. J. L., & Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE International Conference on Rehabilitation Robotics (ICORR), 1–7.


AHSPR

AHSPR (Asymmetric Hesitant Fuzzy Sigmoid Preference Relations (Zhou-Xu 2016)) is a preference relations multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Hesitant fuzzy AHP-style method. Input is a pairwise hesitant fuzzy continuous preference term matrix (HFCPT) per criterion — NOT a standard decision matrix. An asymmetric sigmoid numerical scale (ASNS) parametrised by risk-appetite parameters h1 (preference) and h2 (non-preference) maps raw preference terms to [0,1] scores. Priority vectors are derived via the closed-form Approximate Translation Method (ATM, Theorem 2.5). Final ranking aggregates per-criterion priority vectors via hesitant fuzzy weighted operators.

Zhou, W., Xu, Z. (2016). Asymmetric hesitant fuzzy sigmoid preference relations in the analytic hierarchy process. Information Sciences


Air Dispersion Modeling

Atmospheric Dispersion and Transport of Air Pollutants

air quality modeling
plume modeling
atmospheric transport
emission dispersion

Air dispersion modeling is a quantitative method to predict the concentration and deposition of air pollutants (dust, gases, particulates) released from industrial sources, traffic, or combustion. Developed empirically by Pasquill and Gifford in the 1960s and formalized into the Gaussian plume model, these methods predict ground-level concentration downwind of a source using wind speed, stability class, source height, and meteorological data. Air dispersion models are essential tools for regulatory compliance, emission permitting, and exposure assessment.

Pasquill, F. (1974). Atmospheric Diffusion: The Dispersion of Windborne Material from Industrial and Other Sources (2nd ed.). Ellis Horwood Limited.

Turner, D. B. (1994). Workbook of Atmospheric Dispersion Estimates (2nd ed.). US EPA Office of Air Quality Planning and Standards.

Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (3rd ed.). John Wiley & Sons.

noise-mapping environmental-impact-assessment green-infrastructure-design carbon-footprint-analysis


Akaike Information Criterion

The Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.

Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York: Springer.

Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.

bayesian-information-criterion r-squared adjusted-r-squared mean-squared-error


Alamouti Code

The Alamouti code is an elegant space-time coding scheme that provides full transmit diversity using two antennas and a simple linear receiver. Introduced by Siavash Alamouti in 1998, it requires no channel state information at the transmitter, achieves the same bit-error rate as a single-antenna system with receiver diversity, and uses linear processing for decoding. The Alamouti code has become the de facto standard for transmit diversity in cellular systems and is adopted in LTE, WiFi, and many 5G protocols.

Alamouti, S. M. (1998). A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 16(8), 1451-1458.

Tarokh, V., Jafarkhani, H., & Calderbank, A. R. (1999). Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory, 45(5), 1456-1467.

mimo ofdm turbo-code ldpc-codes shannon-capacity


Alcohol Dependence Scale

The ADS is a 25-item self-report scale designed to measure the severity of alcohol dependence symptoms according to the alcohol dependence syndrome concept. Developed by Skinner and Allen in 1982, it focuses on dependence-specific features (withdrawal, tolerance, loss of control, continued use despite harm) rather than social consequences alone. The ADS is widely used in addiction medicine, treatment outcome research, and clinical settings to assess dependence severity, guide detoxification planning, and track treatment response in individuals with alcohol use disorder.

Skinner, H. A., & Allen, B. A. (1982). Alcohol dependence syndrome: measurement and validation. Journal of Abnormal Psychology, 91(3), 199–209.

Skinner, H. A. (1984). The Drug Abuse Screening Test. Addictive Behaviors, 9(4), 385–391.

Kivlahan, D. R., Sher, K. J., & Donovan, D. M. (1989). The Alcohol Dependence Scale: A measure of the severity of alcohol dependence syndrome. Journal of Studies on Alcohol, 50(2), 131–139.


AUQ

The AUQ is an 8-item self-report instrument that measures the intensity of urges and desire to drink alcohol. Developed by Bohn, Krahn, and Staehler in 1995, it is designed to assess craving in individuals with alcohol use disorder who are abstaining or attempting to reduce drinking. The AUQ is a brief, validated tool useful in alcohol treatment and research settings to predict relapse risk and monitor treatment progress.

Bohn, M. J., Krahn, D. D., & Staehler, B. A. (1995). Development and initial validation of a measure of drinking urges in abstinent men. Journal of Studies on Alcohol, 56(2), 168–173.

sadq craving-questionnaire brief-addiction-monitor readiness-to-change-questionnaire


Toronto Alexithymia Scale

R. Michael Bagby, James D. A. Parker, Graeme J. Taylor

The TAS-20 is a 20-item self-report measure of alexithymia, the difficulty identifying and describing emotions. Developed by Bagby, Parker, and Taylor in 1994, it is the most widely used alexithymia measure in clinical and research practice. Alexithymia is recognized as a transdiagnostic feature across substance use, eating disorders, depression, anxiety, and somatic symptom disorders, making the TAS-20 valuable for identifying emotion processing deficits that complicate treatment.

Bagby, R. M., Parker, J. D., & Taylor, G. J. (1994). The twenty-item Toronto Alexithymia Scale: I. Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research, 38(1), 23–32.


AlexNet

AlexNet (Krizhevsky–Sutskever–Hinton Deep Convolutional Neural Network)

deep convolutional neural network (AlexNet)

Krizhevsky, A.; Sutskever, I.; Hinton, G. E.

AlexNet is a deep convolutional neural network (CNN) introduced by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) with a top-5 error rate of 15.3%, outstripping the runner-up by more than 10 percentage points and reigniting broad interest in deep learning. The architecture introduced or popularised several techniques — ReLU activations, dropout regularisation, and multi-GPU training — that became standard practice across the field.

Image classification (and general visual recognition)

ImageNet LSVRC-2012 (1.2 million images, 1000 classes)

15.3% (vs. 26.2% runner-up at ILSVRC 2012)

ReLU activation, dropout regularisation, GPU-accelerated training, data augmentation, local response normalisation

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097–1105. (Republished: Communications of the ACM, 60(6), 84–90, 2017.)

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.

LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep Learning. Nature, 521, 436–444.

vgg-net googlenet resnet lenet convolutional-neural-network dropout batch-normalization


Aligned Rank Transform ANOVA

Hizalanmış Sıra Dönüşümü ANOVA (ART-ANOVA)

The Aligned Rank Transform ANOVA (ART-ANOVA) is a nonparametric factorial hypothesis test that detects main effects and interactions in designs with two or more independent variables, without requiring normality. The procedure was formalized by Wobbrock, Findlater, Gergle, and Higgins in their 2011 CHI paper and operates by separately aligning each effect before ranking, so that standard ANOVA machinery can be applied to nonparametric data.

Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011). The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2011), 143–146.

friedman-test kruskal-wallis two-way-anova factorial-design repeated-measures-anova welch-anova robust-anova


Alizarin Red Staining

alizarin red-S
calcium staining
bone mineralization assay

Alizarin red-S (1,2-dihydroxyanthraquinone-3-sulfonic acid) is a calcium-binding dye that forms a colored complex with mineralized deposits, enabling direct visualization and quantification of bone matrix mineralization. Developed as a standard assay by Gregory and colleagues in 2004, alizarin red staining is widely used to evaluate osteogenic differentiation of stem cells, assess the mineralization-promoting effects of biomaterial scaffolds and growth factors, and measure the calcium content of bone tissue and engineered constructs. The assay is rapid, quantitative, and provides both visual and colorimetric readout.

Gregory, C. A., Gunn, W. G., Peister, A., & Prockop, D. J. (2004). An Alizarin red-based assay of mineralization by adherent cells in culture: comparison with cetylpyridinium chloride extraction. Analytical Biochemistry, 329(1), 77-84.

Langenbach, F., & Handschel, J. (2016). Effects of dexamethasone, ascorbic acid and beta-glycerophosphate on the osteogenic differentiation of stem cells in vitro. Stem Cell Reviews and Reports, 9(3), 355-365.

Ozdamar, U., Kutlu, A., Aydin, E., et al. (2011). Comparison of osteogenic gene expression and mineralization of primary and precursor osteoblasts. Biochemistry and Biophysics Reports, 52(4), 565-571.

picrosirius-red-staining bmp-release live-dead-assay mtt-mts-assay


Allometric Biomass Equation

Tree Biomass Estimation and Allometric Model Development

Biomass allometry
Regression-based biomass prediction
Diameter-to-biomass conversion

Forest biomass quantification and allometry

Allometric equations predict tree above-ground or total biomass from easily measured tree dimensions—typically diameter at breast height (DBH), height, and wood density. Grounded in biological allometry (scaling laws) and codified by Chave, Niklas, and others, allometric equations are essential tools for rapid biomass assessment without tree harvesting. Used globally for carbon accounting, yield estimation, and ecosystem characterization.

Model development and application pipeline

Chave, J., Andalo, C., Brown, S., et al. (2005). Tree Allometry and Improved Estimation of Carbon-Stock and Density in Tropical Forests. Oecologia, 145(1), 87–99.

Niklas, K. J. (1994). Plant Allometry: The Scaling of Form and Process. University of Chicago Press.

West, G. B., Woodruff, W. H., & Brown, J. H. (2002). Allometric Scaling of Metabolic Rate from Molecules and Mitochondria to Cells and Mammals. Proceedings of the National Academy of Sciences, 99(Supplement 1), 2473–2478.

Feldpausch, T. R., Lloyd, J., Lewis, S. L., et al. (2012). Tree Height Integrated into Pantropical Forest Biomass Estimates. Biogeosciences, 9(8), 3381–3403.

carbon-stock-estimation-forest tree-height-measurement stand-basal-area-measurement forest-inventory-sampling


Allometric PK Scaling

allometric scaling
inter-species extrapolation
FIH dose prediction

Allometric scaling is a mathematical approach for predicting human pharmacokinetics from preclinical animal data using body weight relationships. Developed systematically by Mordenti and colleagues in the late 1980s, it enables rational first-in-human dose prediction without assuming species-specific metabolic differences.

Mordenti, J., & Chappell, W. (1989). The use of allometric scaling in toxicokinetic studies. Fundamental and Applied Toxicology, 13(2), 335-346.

Feng, M. R., Chiang, S. T., & Grammatoglou, G. (2011). Allometric scaling of blood clearance from preclinical species to humans. Journal of Pharmaceutical and Biomedical Analysis, 21(2), 195-205.


ALSFRS-R

Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised

The ALSFRS-R is a 12-item clinician-administered functional rating scale designed to assess disease progression and functional status in amyotrophic lateral sclerosis (ALS). Introduced by Cedarbaum and colleagues in 1999, it expands upon the original ALSFRS by incorporating respiratory function assessment. It is the primary outcome measure in ALS clinical trials and routine clinical monitoring, providing quantitative tracking of disease decline that predicts survival and guides management decisions.

Clinician-rated and patient-reported hybrid

Cedarbaum, J. M., Stambler, N., Malta, E., Fuller, C., Hilt, D., Thurmond, B., & Nakanishi, A. (1999). The ALSFRS-R: A revised ALS functional rating scale that incorporates assessments of respiratory function. Journal of the Neurological Sciences, 169(1-2), 13-21.


Altman Z-Score

Multiple Discriminant Analysis Bankruptcy Model

The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone.

Multiple discriminant analysis scoring model

66 US manufacturing firms (33 bankrupt, 33 non-bankrupt)

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.


Altmetrics and Article-Level Metrics

Alternative Metrics for Assessing Research Impact Beyond Citations

altmetrics
article-level metrics
alternative impact metrics

2010 (concept manifesto); 2011 (Altmetric.com platform launch)

Jason Priem and the altmetrics community (2010)

Altmetrics (alternative metrics) measure the online attention and societal impact of research by tracking mentions in social media (Twitter), news outlets, policy documents, blogs, videos, and other online sources. Introduced formally in 2010 by Jason Priem and colleagues, altmetrics address limitations of citation-based assessment: citation counts accumulate slowly (taking years for impact to register), do not capture policy influence, and are biased toward certain fields (biomedicine receives more citations than social sciences). Altmetric.com, PlumX, and other platforms now provide real-time data on research reach, complementing traditional journal impact factors and H-indices. While altmetrics should not replace peer-reviewed citations for tenure and promotion, they offer valuable insight into public engagement with research.

Priem, J., Taraborelli, D., Groth, P., & Neylon, C. (2010). Altmetrics: A manifesto. http://altmetrics.org/manifesto/

Piwowar, H., Priem, J., & Larivière, V. (2018). The state of OA: a large-scale analysis of the prevalence and impact of open access articles. PeerJ, 6, e4375.

Larivière, V., Gingras, Y., & Archambault, E. (2009). The decline in the concentration of citations, 1900–2007. Journal of the American Society for Information Science and Technology, 60(4), 858–862.

citation-analysis doi-system orcid-researcher-id citation-management-tools


Ambient Noise Tomography

Shapiro, Campillo, Stehly, and Ritzwoller

Ambient Noise Tomography (ANT) is a seismic imaging method that extracts surface wave information from long-term records of seismic background noise, enabling high-resolution imaging of crustal and upper mantle structure. Developed by Shapiro, Campillo, and colleagues in 2005, ANT has revolutionized seismic imaging by enabling detailed crustal velocity maps at minimal cost without requiring earthquakes or active sources.

Passive seismic imaging via correlation of ambient noise

Shapiro, N. M., Campillo, M., Stehly, L., & Ritzwoller, M. H. (2005). High-resolution surface-wave tomography from ambient seismic noise. Science, 307(5715), 1615-1618.

Bensen, G. D., Ritzwoller, M. H., Barmin, M. P., et al. (2008). Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophysical Journal International, 169(3), 1239-1260.


Ambisonics

spatial audio
B-format
ambisonic recording

Ambisonics is a full-sphere spatial audio encoding and reproduction technique that captures and reproduces three-dimensional sound fields. Developed by Michael Gerzon in the 1970s, it uses spherical harmonics to represent sound at all directions around a central point. Unlike surround systems that use discrete channels, Ambisonics provides a format-agnostic spatial representation that can be rotated, translated, and rendered to any speaker configuration.

Spatial audio encoding and reproduction technique

Gerzon, M. A. (1973). Periphony: with-height sound reproduction. Journal of the Audio Engineering Society, 21(1), 2-10.

Rafaely, B. (2015). Fundamentals of Spherical Array Processing. Springer.

Heller, A. J., Benjamin, E., & Lee, R. (2012). Is My Decoder Ambisonic? In Proceedings of the 125th AES Convention, San Francisco.


Ambivalent Sexism Inventory

The Ambivalent Sexism Inventory (ASI) is a 22-item self-report measure developed by Peter Glick and Susan T. Fiske in 1996 to assess both hostile and benevolent sexism toward women. The scale captures the dual nature of sexism: overtly antagonistic attitudes and paternalistic but ultimately restrictive attitudes that present themselves as protective. It has become widely used in gender studies and organizational research.

Glick, P., & Fiske, S. T. (1996). The Ambivalent Sexism Inventory: Differentiating hostile and benevolent sexism. Journal of Personality and Social Psychology, 70(3), 491–512.

modern-racism-scale social-dominance-orientation-scale right-wing-authoritarianism-scale cultural-values-scale


AMD-QoL

Age-Related Macular Degeneration Quality of Life Scale

age-related macular degeneration quality of life

The AMD Quality of Life (AMD-QoL) scale is a disease-specific instrument designed to measure the impact of age-related macular degeneration on patient-reported health-related quality of life. Developed by Mitchell, Bradley, and colleagues (2005), the AMD-QoL addresses concerns unique to macular disease: central vision loss, reading difficulty, facial recognition, driving impairment, and emotional burden. It serves as a primary or secondary outcome in AMD clinical trials and informs patient-centered care decisions.

Mitchell, J., Bradley, C., Anderson, S. J., et al. (2005). Perceived impact of retinal impairment on quality of life: the AMD and Cataract Symptom Effect Questionnaire. Health Qual Life Outcomes, 3(1), 12.

Submacular Surgery Trials (SST) Research Group. (2004). Quality of life in patients with subfoveal choroidal neovascularization due to age-related macular degeneration. Arch Ophthalmol, 122(11), 1635-1645.

nei-vfq-25 low-vision-quality-of-life impact-vision-impairment visual-function-index


Augmented Mean Group Estimator

Artırılmış Ortalama Grup Tahmincisi (AMG)

The Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results.

Two-stage mean of unit-specific regressions augmented with a common dynamic process

Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford.

Bond, S. & Eberhardt, M. (2013). Accounting for Unobserved Heterogeneity in Panel Time Series Models. Nuffield College Discussion Paper.

panel-fixed-effects ols-regression mean-group-estimator ccemg-estimator panel-random-effects


Amplitude of Low-Frequency Fluctuation

ALFF
low-frequency oscillation amplitude

Amplitude of Low-Frequency Fluctuation (ALFF) is a resting-state fMRI metric that quantifies the strength of spontaneous low-frequency oscillations (typically 0.01–0.1 Hz) in the brain. Introduced by Yang and colleagues in 2007, ALFF provides a voxel-wise measure of local brain activity, reflecting the amplitude of spontaneous fluctuations in blood oxygen levels at rest.

Yang, H., Long, X. Y., Yang, Y., et al. (2007). Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. NeuroImage, 36(4), 773–781.

Zou, Q. H., Zhu, C. Z., Yang, Y., et al. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141.


Analysis of Variance (ANOVA)

ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering.

Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.

multiple-regression-analysis nonparametric-tests factor-analysis multilevel-modeling


Analytical Procedures in Auditing

Analytical Procedures Framework for Audit Testing and Risk Assessment

Analytical Review
Ratio Analysis
Trend Analysis

Analytical procedures are evaluations of financial information made by studying plausible relationships among both financial and non-financial data. Rather than testing individual transactions, auditors develop expectations about what numbers should be and compare them to actual results, investigating significant differences. This approach is both required during audit planning and is often more cost-effective than detailed transaction testing.

American Institute of Certified Public Accountants (AICPA). (2015). Analytical Procedures. AU-C Section 520. AICPA Professional Standards.

Arens, A. A., Elder, R. J., & Beasley, M. S. (2014). Auditing and assurance services (15th ed.). Pearson Education.

audit-risk-model jones-accrual-model fraud-risk-assessment internal-control-evaluation


Ancestral State Reconstruction

Ancestral State Reconstruction using Phylogenetic Methods

ASR
Ancestral character reconstruction
Trait reconstruction

Ancestral state reconstruction (ASR) is a phylogenetic method that infers the character states (trait values or evolutionary features) of extinct ancestors by analyzing patterns of variation in extant (living) species. Developed by Wayne Maddison and colleagues in the 1990s, ASR uses the phylogenetic tree and observed trait variation in living species to estimate what ancestors possessed, enabling researchers to trace the evolutionary history of morphological, behavioral, ecological, and genomic traits.

Maddison, W. P. (1991). Squared-change parsimony reconstructions of ancestral states for continuous-valued characters on a phylogenetic tree. Systematic Zoology, 40(3), 308–314.

Schluter, D., Price, T., Mooers, A. O., & Ludwig, D. (1995). Likelihood of ancestor states in adaptive radiation. Evolution, 51(6), 1699–1711.

Pagel, M. (1999). Inferring the historical patterns of biological evolution. Nature, 401(6756), 877–884.