A new integrated model of noncompensatory and compensatory decision strategies

Abstract We describe and test a model that captures conjunctive, disjunctive, and compensatory judgment and choice strategies, as well as selected hybrid combinations of these. This model: (a) can be estimated solely from nonexperimental outcome data, (b) remains true to the conceptualization of noncompensatory heuristics as cognitively less demanding for decision makers, (c) is truly noncompensatory and not just approximately, (d) tests for a “pervasive” influence of cutoffs, (e) allows for the possibility that decision makers use different strategies across attributes, and (f) provides a more plausible account of behavior than competing models. We show empirically that decision makers may sometimes devalue objects for almost failing a conjunctive criterion or value objects more favorably for almost passing a disjunctive criterion—what we term a pervasive influence of a cutoff. The superiority of the proposed model relative to two other state-of-the-art models is demonstrated using both actual admit/reject decisions of an MBA admissions office as well as 10 simulations of various decision tasks.

[1]  J. Shanteau,et al.  Livestock judges: How much information can an expert use? , 1978 .

[2]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[3]  Christopher Winship,et al.  Logit and Probit: Ordered and Multinomial Models , 2003 .

[4]  H. Halff Choice theories for differentially comparable alternatives , 1976 .

[5]  R. Luce Semiorders and a Theory of Utility Discrimination , 1956 .

[6]  H. J. Einhorn,et al.  Linear regression and process-tracing models of judgment. , 1979 .

[7]  Noreen M. Klein,et al.  An Investigation of Utility-Directed Cutoff Selection , 1987 .

[8]  I. J. Schoenberg Contributions to the problem of approximation of equidistant data by analytic functions. Part A. On the problem of smoothing or graduation. A first class of analytic approximation formulae , 1946 .

[9]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[10]  Timothy D. Wilson,et al.  Telling more than we can know: Verbal reports on mental processes. , 1977 .

[11]  R. Dawes SOCIAL SELECTION BASED ON MULTIDIMENSIONAL CRITERIA. , 1964, Journal of abnormal psychology.

[12]  John Roberts,et al.  A Grounded Model of Consideration Set Size and Composition , 1989 .

[13]  Robert A. Lordo,et al.  Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.

[14]  E. Brunswik,et al.  Thing constancy as measured by correlation coefficients. , 1940 .

[15]  Benjamin Czaczkes,et al.  On Detecting Nonlinear Noncompensatory Judgment Strategies: Comparison of Alternative Regression Models , 1995 .

[16]  G. Debreu Mathematical Economics: Representation of a preference ordering by a numerical function , 1983 .

[17]  Lewis R. Goldberg,et al.  Five models of clinical judgment: An empirical comparison between linear and nonlinear representations of the human inference process , 1971 .

[18]  K. Lancaster A New Approach to Consumer Theory , 1966, Journal of Political Economy.

[19]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[20]  Daniel S. Levine,et al.  Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology , 1996 .

[21]  Carl F. Mela,et al.  Using fuzzy set theoretic techniques to identify preference rules from interactions in the linear model: an empirical study , 1995 .

[22]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[23]  Mary Frances Luce,et al.  Behavioral Decision ResearchAn Overview , 1998 .

[24]  D. B. Yntema,et al.  Man-Computer Cooperation in Decisions Requiring Common Sense , 1961 .

[25]  L. Beach Broadening the Definition of Decision Making: The Role of Prechoice Screening of Options , 1993 .

[26]  R. Luce,et al.  The Representational Measurement Approach to Psychophysical and Judgmental Problems , 1998 .

[27]  Frederick Mosteller,et al.  An Experimental Measurement of Utility , 1951, Journal of Political Economy.

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

[29]  Irwin P. Levin,et al.  Phased Narrowing: A New Process Tracing Method for Decision Making , 1995 .

[30]  B. G. Quinn,et al.  The determination of the order of an autoregression , 1979 .

[31]  P. Schoemaker Experiments on Decisions under Risk: The Expected Utility Hypothesis , 1980 .

[32]  Paul E. Green,et al.  Completely Unacceptable Levels in Conjoint Analysis: A Cautionary Note , 1988 .

[33]  Mary Frances Luce,et al.  Behavioral Decision Research: An Overview , 1998 .

[34]  S. Grossberg How does a brain build a cognitive code , 1980 .

[35]  Steven M. Shugan The Cost Of Thinking , 1980 .

[36]  Makoto Abe,et al.  A Generalized Additive Model for Discrete-Choice Data , 1999 .

[37]  H. J. Einhorn Use of nonlinear, noncompensatory models as a function of task and amount of information , 1971 .

[38]  D. Gensch A Two-Stage Disaggregate Attribute Choice Model , 1987 .

[39]  John O. Summers,et al.  A Comparison of Linear and Nonlinear Evaluation Process Models , 1975 .

[40]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[41]  Michael H. Birnbaum,et al.  Measurement, judgment, and decision making , 1998 .

[42]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[43]  Gerd Gigerenzer,et al.  Adaptive Thinking: Rationality in the Real World , 2000 .

[44]  J. Douglas Carroll,et al.  Toward a new paradigm for the study of multiattribute choice behavior: Spatial and discrete modeling of pairwise preferences. , 1991 .

[45]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[46]  H. D. Block,et al.  Random Orderings and Stochastic Theories of Responses (1960) , 1959 .

[47]  A. Tversky Elimination by aspects: A theory of choice. , 1972 .

[48]  Refik Soyer,et al.  Bayesian Methods for Nonlinear Classification and Regression , 2004, Technometrics.

[49]  Michael T. Brannick,et al.  Nonlinear and noncompensatory processes in performance evaluation , 1989 .

[50]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[51]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[52]  Joel Huber,et al.  Adapting Cutoffs to the Choice Environment: The Effects of Attribute Correlation and Reliability , 1991 .

[53]  R. Ratcliff,et al.  Multialternative decision field theory: a dynamic connectionist model of decision making. , 2001, Psychological review.

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

[55]  David A. Ratkowsky,et al.  Handbook of nonlinear regression models , 1990 .

[56]  Adele Diederich,et al.  Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time , 2003 .

[57]  Greg M. Allenby,et al.  A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules , 2004 .

[58]  Jean-Claude Falmagne,et al.  A representation theorem for finite random scale systems , 1978 .

[59]  A. Bröder Decision making with the "adaptive toolbox": influence of environmental structure, intelligence, and working memory load. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[60]  S. Grossberg,et al.  Neural dynamics of decision making under risk: affective balance and cognitive-emotional interactions. , 1988, Psychological review.

[61]  D. McFadden Econometric Models of Probabilistic Choice , 1981 .

[62]  Clyde H. Coombs,et al.  Nonmetric factor analysis , 1955 .

[63]  P. Zarembka Frontiers in econometrics , 1973 .

[64]  B. Mellers,et al.  Similarity and Choice. , 1994 .

[65]  Paul Slovic,et al.  Comparison of Bayesian and Regression Approaches to the Study of Information Processing in Judgment. , 1971 .

[66]  P. Hammond,et al.  Handbook of Utility Theory , 2004 .

[67]  Joffre Swait,et al.  A NON-COMPENSATORY CHOICE MODEL INCORPORATING ATTRIBUTE CUTOFFS , 2001 .

[68]  John W. Payne,et al.  Task complexity and contingent processing in decision making: An information search and protocol analysis☆ , 1976 .

[69]  M. Brucks The Effects of Product Class Knowledge on Information Search Behavior , 1985 .

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

[71]  Stuart Dillon,et al.  Descriptive Decision Making : Comparing Theory with Practice , 1960 .

[72]  W. R. Buckland,et al.  Contributions to Probability and Statistics , 1960 .

[73]  J. Ford,et al.  Process tracing methods: Contributions, problems, and neglected research questions , 1989 .

[74]  Robert S. Billings,et al.  Measures of compensatory and noncompensatory models of decision behavior: Process tracing versus policy capturing , 1983 .

[75]  D. Cox,et al.  Discrimination between alternative binary response models. , 1967, Biometrika.

[76]  H. J. Einhorn The use of nonlinear, noncompensatory models in decision making. , 1970, Psychological bulletin.