Optimal cue aggregation in the absence of criterion knowledge

J Behav Dec Making. 2019;1–16. Abstract The study of multi‐cue judgment investigates how decision makers aggregate cues to predict the value of a criterion variable. We consider a multi‐cue judgment task in which decision makers have prior knowledge of inter‐cue relationships but are ignorant of how the cues correlate with the criterion. In this setting, a naive judgment strategy prescribes weighting the cues equally. Although many participants are well described via an equal weighting scheme, we find that a substantial minority of participants make predictions consistent with a weighting scheme based on a low‐ dimensional projection of the cue space that optimally takes into account inter‐cue correlations. The use of such a weighting scheme is consistent with minimizing maximal error in prediction when the cue‐criterion relationships are unknown.

[1]  J. Tenenbaum,et al.  Optimal Predictions in Everyday Cognition , 2006, Psychological science.

[2]  P. Juslin,et al.  Information integration in multiple cue judgment: A division of labor hypothesis , 2008, Cognition.

[3]  S. Broomell,et al.  Why Are Experts Correlated? Decomposing Correlations Between Judges , 2009 .

[4]  Julian N. Marewski,et al.  Using the ACT-R architecture to specify 39 quantitative process models of decision making , 2011, Judgment and Decision Making.

[5]  Graham K. Rand,et al.  Quantitative Applications in the Social Sciences , 1983 .

[6]  Gerd Gigerenzer,et al.  Heuristic decision making. , 2011, Annual review of psychology.

[7]  Ilan Yaniv,et al.  The Benefit of Additional Opinions , 2004 .

[8]  P. Todd,et al.  Simple Heuristics That Make Us Smart , 1999 .

[9]  S. Bonaccio,et al.  What types of advice do decision-makers prefer? , 2010 .

[10]  E. Brunswik,et al.  The Conceptual Framework of Psychology , 1954 .

[11]  Lisa Werner,et al.  Principles of forecasting: A handbook for researchers and practitioners , 2002 .

[12]  D. Budescu,et al.  The dominance analysis approach for comparing predictors in multiple regression. , 2003, Psychological methods.

[13]  M. R. Novick,et al.  Statistical Theories of Mental Test Scores. , 1971 .

[14]  Peter C. Fishburn,et al.  LEXICOGRAPHIC ORDERS, UTILITIES AND DECISION RULES: A SURVEY , 1974 .

[15]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[16]  Jack B. Soll.  Intuitive Theories of Information: Beliefs about the Value of Redundancy , 1999, Cognitive Psychology.

[17]  Daniel Kahneman,et al.  Availability: A heuristic for judging frequency and probability , 1973 .

[18]  John W. Payne,et al.  The adaptive decision maker: Name index , 1993 .

[19]  D. Goldstein,et al.  How good are simple heuristics , 1999 .

[20]  Janet A. Sniezek,et al.  Cueing and Cognitive Conflict in Judge-Advisor Decision Making , 1995 .

[21]  C. Davis-Stober,et al.  A Geometric Analysis of When Fixed Weighting Schemes Will Outperform Ordinary Least Squares , 2011, Psychometrika.

[22]  David V. Budescu,et al.  A Constrained Linear Estimator for Multiple Regression , 2010 .

[23]  Janet A. Sniezek,et al.  Trust, Confidence, and Expertise in a Judge-Advisor System. , 2001, Organizational behavior and human decision processes.

[24]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[25]  Arndt Bröder,et al.  Cognitive processes, models and metaphors in decision research , 2008, Judgment and Decision Making.

[26]  M H Birnbaum,et al.  Combining information from sources that vary in credibility , 1976, Memory & cognition.

[27]  H. Raiffa,et al.  Decisions with Multiple Objectives , 1993 .

[28]  James L. McClelland,et al.  Learning hierarchical category structure in deep neural networks , 2013 .

[29]  B. Armelius,et al.  The effect of cue-criterion correlations, cue intercorrelations and the sign of the cue intercorrelation on performance in suppressor variable tasks , 1976 .

[30]  H. Akaike.  Fitting autoregressive models for prediction , 1969 .

[31]  A. Glöckner,et al.  What is adaptive about adaptive decision making? A parallel constraint satisfaction account , 2014, Cognition.

[32]  David V. Budescu,et al.  Why recognition is rational: Optimality results on single-variable decision rules , 2010, Judgment and Decision Making.

[33]  Estes Wk.  The problem of inference from curves based on group data. , 1956 .

[34]  E. Wagenmakers,et al.  AIC model selection using Akaike weights , 2004, Psychonomic bulletin & review.

[35]  Clintin P. Davis-Stober,et al.  Rational Foundations of Fast and Frugal Heuristics: The Ecological Rationality of Strategy Selection via Improper Linear Models , 2015, Minds and Machines.

[36]  Laureen A. Maines.  An experimental examination of subjective forecast combination , 1996 .

[37]  Sudeep Bhatia,et al.  Decision Making in Environments with Non‐Independent Dimensions , 2018 .

[38]  N. Harvey,et al.  Combining Advice: The Weight of a Dissenting Opinion in the Consensus , 2004 .

[39]  Nigel Harvey,et al.  Combining forecasts: What information do judges need to outperform the simple average? , 1999 .

[40]  Seth Bullock,et al.  Simple Heuristics That Make Us Smart , 1999 .

[41]  Adrian K. Rantilla,et al.  Confidence in aggregation of expert opinions. , 2000, Acta psychologica.

[42]  U. Hoffrage,et al.  Fast, frugal, and fit: Simple heuristics for paired comparison , 2002 .

[43]  John R. Anderson.  The Adaptive Character of Thought , 1990 .

[44]  W. Estes.  The problem of inference from curves based on group data. , 1956, Psychological bulletin.

[45]  J. Tenenbaum,et al.  Theory-based Bayesian models of inductive learning and reasoning , 2006, Trends in Cognitive Sciences.

[46]  Ilan Yaniv.  ORGANIZATIONAL BEHAVIOR AND HUMAN DECISION PROCESSES, VOLUME 69, ISSUE 3 , 1997 .

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

[48]  Adrian K. Rantilla,et al.  The effects of asymmetry among advisors on the aggregation of their opinions , 2003 .

[49]  J. Klayman.  Cue discovery in probabilistic environments: Uncertainty and experimentation. , 1988 .

[50]  S. Bonaccio,et al.  Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences , 2006 .

[51]  Daniel M. Oppenheimer,et al.  Information processing as a paradigm for decision making. , 2015, Annual review of psychology.

[52]  Varun Gauria,et al.  Organizational Behavior and Human Decision Processes , 2019 .

[53]  A. Raftery.  Bayesian Model Selection in Social Research , 1995 .

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

[55]  J. Scott Armstrong,et al.  Principles of forecasting , 2001 .

[56]  R. Dawes.  Judgment under uncertainty: The robust beauty of improper linear models in decision making , 1979 .

[57]  R. Dawes,et al.  Heuristics and Biases: Clinical versus Actuarial Judgment , 2002 .

[58]  Van Swol,et al.  Forecasting another’s enjoyment versus giving the right answer: Trust, shared values, task effects, and confidence in improving the acceptance of advice , 2011 .