An Ontology of Decision Models

Decision models are essential theoretical tools in the study of choice behavior, but there is little consensus about the best model for describing choice, with different fields and different research programs favoring their own idiosyncratic sets of models. Even within a given field, decision models are seldom studied alongside each other, and insights obtained using 1 model are not typically generalized to others. We present the results of a large-scale computational analysis that uses landscaping techniques to generate a representational structure for describing decision models. Our analysis includes 89 prominent models of risky and intertemporal choice, and results in an ontology of decision models, interpretable in terms of model spaces, clusters, hierarchies, and graphs. We use this ontology to measure the properties of individual models and quantify the relationships between different models. Our results show how decades of quantitative research on human choice behavior can be synthesized within a single representational framework. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

[1]  Daniel R. Cavagnaro,et al.  Discriminating among probability weighting functions using adaptive design optimization , 2013, Journal of risk and uncertainty.

[2]  E. Kay,et al.  Introductory Graph Theory , 1978 .

[3]  R. Hertwig,et al.  The priority heuristic: making choices without trade-offs. , 2006, Psychological review.

[4]  Clyde H. Coombs,et al.  Components of risk in decision making: Probability and variance preferences. , 1960 .

[5]  Eric J. Johnson,et al.  Mindful judgment and decision making. , 2009, Annual review of psychology.

[6]  J. Schreiber.  Foundations Of Statistics , 2016 .

[7]  M. Birnbaum,et al.  New Paradoxes of Risky Decision Making , 2022 .

[8]  P. Delquié,et al.  Disappointment without prior expectation: a unifying perspective on decision under risk , 2006 .

[9]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[10]  Samuel M. McClure,et al.  Separate Neural Systems Value Immediate and Delayed Monetary Rewards , 2004, Science.

[11]  R. M. Adelson,et al.  Utility Theory for Decision Making , 1971 .

[12]  L. Green,et al.  Temporal discounting when the choice is between two delayed rewards. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[13]  L. Green,et al.  A discounting framework for choice with delayed and probabilistic rewards. , 2004, Psychological bulletin.

[14]  Falk Lieder,et al.  Overrepresentation of Extreme Events in Decision Making Reflects Rational Use of Cognitive Resources , 2017, Psychological review.

[15]  Samuel M. McClure,et al.  Time Discounting for Primary Rewards , 2007, The Journal of Neuroscience.

[16]  Daniel Read,et al.  Discounting by Intervals: A Generalized Model of Intertemporal Choice , 2006, Manag. Sci..

[17]  W. Thorngate.  Efficient decision heuristics. , 1980 .

[18]  Jay I. Myung,et al.  Assessing the distinguishability of models and the informativeness of data , 2004, Cognitive Psychology.

[19]  Roger Ratcliff,et al.  Assessing model mimicry using the parametric bootstrap , 2004 .

[20]  G. Loomes.  Modeling choice and valuation in decision experiments. , 2010, Psychological review.

[21]  R. Hertwig,et al.  Prospect Theory Reflects Selective Allocation of Attention , 2018, Journal of experimental psychology. General.

[22]  E. Rowland.  Theory of Games and Economic Behavior , 1946, Nature.

[23]  S. Broomell,et al.  Pair-wise comparisons of multiple models , 2011, Judgment and Decision Making.

[24]  W. Edwards.  The prediction of decisions among bets. , 1955, Journal of experimental psychology.

[25]  Uday S. Karmarkar,et al.  Subjectively weighted utility: A descriptive extension of the expected utility model , 1978 .

[26]  R. Sibson.  Studies in the Robustness of Multidimensional Scaling: Procrustes Statistics , 1978 .

[27]  George Wu,et al.  Testing Prospect Theories Using Probability Tradeoff Consistency , 2005 .

[28]  R. Hertwig,et al.  How the twain can meet: Prospect theory and models of heuristics in risky choice , 2017, Cognitive Psychology.

[29]  Robert Sugden,et al.  Incorporating a stochastic element into decision theories , 1995 .

[30]  M. F. Luce,et al.  Constructive Consumer Choice Processes , 1998 .

[31]  Jeroen van de Ven,et al.  Aspiration Level, Probability of Success and Failure, and Expected Utility , 2008 .

[32]  Eric J. Johnson,et al.  Adaptive Strategy Selection in Decision Making. , 1988 .

[33]  P. Fishburn.  Mean-Risk Analysis with Risk Associated with Below-Target Returns , 1977 .

[34]  Daniel Read,et al.  DRIFT: an analysis of outcome framing in intertemporal choice. , 2013, Journal of experimental psychology. Learning, memory, and cognition.

[35]  Claudia González-Vallejo,et al.  Attribute-wise vs. alternative-wise mechanism in intertemporal choice: Testing the proportional difference, trade-off, and hyperbolic models. , 2016 .

[36]  A. Rubinstein.  Similarity and decision-making under risk (is there a utility theory resolution to the Allais paradox?) , 1988 .

[37]  Colin Camerer,et al.  Neuroeconomics: decision making and the brain , 2008 .

[38]  Ido Erev,et al.  Noisy retrieval models of over- and undersensitivity to rare events , 2015 .

[39]  Robert L. Slonim,et al.  Combining a Theoretical Prediction with Experimental Evidence , 2002 .

[40]  K. Mukherjee.  A dual system model of preferences under risk. , 2010, Psychological review.

[41]  Jay I. Myung,et al.  Optimal experimental design for model discrimination. , 2009, Psychological review.

[42]  M. Kelly,et al.  The TIPPME intervention typology for changing environments to change behaviour , 2017, Nature Human Behaviour.

[43]  R. Luce,et al.  Ranked Additive Utility Representations of Gambles: Old and New Axiomatizations , 2004 .

[44]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[45]  P. Killeen.  An additive-utility model of delay discounting. , 2009, Psychological review.

[46]  A. Shleifer,et al.  Salience Theory of Choice Under Risk , 2010 .

[47]  A. Witte,et al.  The Influence of Probability on Risky Choice: A Parametric Examination , 1992 .

[48]  Richard Gonzalez,et al.  On the Shape of the Probability Weighting Function , 1999, Cognitive Psychology.

[49]  E. Weber,et al.  Predicting Risk-Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation , 2004, Psychological review.

[50]  P. Glimcher,et al.  Neuroeconomics: The Consilience of Brain and Decision , 2004, Science.

[51]  R. Sugden,et al.  Regret Theory: An alternative theory of rational choice under uncertainty Review of Economic Studies , 1982 .

[52]  M. Munafo,et al.  Delayed reward discounting and addictive behavior: a meta-analysis , 2011, Psychopharmacology.

[53]  I. J. Myung,et al.  Counting probability distributions: Differential geometry and model selection , 2000, Proc. Natl. Acad. Sci. USA.

[54]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[55]  David Laibson,et al.  Money Earlier or Later? Simple Heuristics Explain Intertemporal Choices Better Than Delay Discounting Does , 2015, Psychological science.

[56]  J. Townsend,et al.  Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. , 1993, Psychological review.

[57]  J. Dyer,et al.  Relative risk—value models , 1997 .

[58]  Peter H. M. P. Roelofsma,et al.  Modelling intertemporal choices: An anomaly approach. , 1996 .

[59]  I. Erev,et al.  From Anomalies to Forecasts: Toward a Descriptive Model of Decisions Under Risk, Under Ambiguity, and From Experience , 2017, Psychological review.

[60]  A. Tversky.  Intransitivity of preferences. , 1969 .

[61]  Sudeep Bhatia.  Sequential sampling and paradoxes of risky choice , 2014, Psychonomic Bulletin & Review.

[62]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[63]  D. Prelec.  The Probability Weighting Function , 1998 .

[64]  Jane E. J. Ebert,et al.  The Fragility of Time: Time-Insensitivity and Valuation of the Near and Far Future , 2007, Manag. Sci..

[65]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[66]  I. J. Myung,et al.  Toward a method of selecting among computational models of cognition. , 2002, Psychological review.

[67]  Jonathan W. Leland.  Generalized similarity judgments: An alternative explanation for choice anomalies , 1994 .

[68]  G. Loewenstein,et al.  Modeling the interplay between affect and deliberation. , 2015 .

[69]  Jagdish Handa,et al.  Risk, Probabilities, and a New Theory of Cardinal Utility , 1977, Journal of Political Economy.

[70]  D. Bernoulli.  Exposition of a New Theory on the Measurement of Risk , 1954 .

[71]  Michel Regenwetter,et al.  Heterogeneity and Parsimony in Intertemporal Choice , 2017 .

[72]  Pavlo R. Blavatskyy,et al.  Models of Stochastic Choice and Decision Theories: Why Both are Important for Analyzing Decisions , 2008 .

[73]  Jess Benhabib,et al.  Present-bias, quasi-hyperbolic discounting, and fixed costs , 2010, Games Econ. Behav..

[74]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[75]  M. Allais.  Le comportement de l'homme rationnel devant le risque : critique des postulats et axiomes de l'ecole americaine , 1953 .

[76]  W. Viscusi.  Prospective reference theory: Toward an explanation of the paradoxes , 1989 .

[77]  J. Busemeyer,et al.  A probabilistic, dynamic, and attribute-wise model of intertemporal choice. , 2014, Journal of experimental psychology. General.

[78]  Michael H. Birnbaum.  Violations of monotonicity in judgment and decision making. , 1997 .

[79]  Ulrike Goldschmidt.  Advances In Behavioral Economics , 2016 .

[80]  I. Ritov,et al.  Emotion-based choice , 1999 .

[81]  J. E. Mazur.  An adjusting procedure for studying delayed reinforcement. , 1987 .

[82]  Jay I. Myung,et al.  On the functional form of temporal discounting: An optimized adaptive test , 2016, Journal of risk and uncertainty.

[83]  P. Samuelson.  A Note on Measurement of Utility , 1937 .

[84]  David E. Bell,et al.  Regret in Decision Making under Uncertainty , 1982, Oper. Res..

[85]  Charles A. Holt,et al.  Risk Aversion and Incentive Effects , 2002 .

[86]  Patrick G. Bissett,et al.  Uncovering the structure of self-regulation through data-driven ontology discovery , 2019, Nature Communications.

[87]  Stephen B. Broomell,et al.  Parameter recovery for decision modeling using choice data. , 2014 .

[88]  P. Glimcher,et al.  An "as soon as possible" effect in human intertemporal decision making: behavioral evidence and neural mechanisms. , 2010, Journal of neurophysiology.

[89]  H. Rachlin.  Notes on discounting. , 2006, Journal of the experimental analysis of behavior.

[90]  Jonathan W. Leland.  SIMILARITY JUDGMENTS AND ANOMALIES IN INTERTEMPORAL CHOICE , 2002 .

[91]  R. Hogarth,et al.  Venture Theory: A Model of Decision Weights , 1990 .

[92]  G. Loewenstein,et al.  Anomalies in Intertemporal Choice: Evidence and an Interpretation , 1992 .

[93]  C. Starmer.  Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk , 2000 .

[94]  J. R. Koehler,et al.  Modern Applied Statistics with S-Plus. , 1996 .

[95]  D. Read,et al.  The Psychology of Intertemporal Tradeoffs , 2009, Psychological review.

[96]  Timothy J. Pleskac,et al.  Ecologically rational choice and the structure of the environment. , 2014, Journal of experimental psychology. General.

[97]  M. Yaari.  The Dual Theory of Choice under Risk , 1987 .

[98]  J. Rieskamp.  The probabilistic nature of preferential choice. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[99]  David E. Bell,et al.  Disappointment in Decision Making Under Uncertainty , 1985, Oper. Res..

[100]  Daniel Read,et al.  Weighing Outcomes by Time or Against Time? Evaluation Rules in Intertemporal Choice , 2014, Cogn. Sci..

[101]  Robert Sugden,et al.  Disappointment and Dynamic Consistency in Choice under Uncertainty , 1986 .

[102]  J. Ramsay,et al.  Matrix correlation , 1984 .

[103]  U. Indahl,et al.  A similarity index for comparing coupled matrices , 2018, Journal of Chemometrics.

[104]  J. Kruskal.  Nonmetric multidimensional scaling: A numerical method , 1964 .