Sequentially simulated outcomes: kind experience versus nontransparent description.

Recently, researchers have investigated differences in decision making based on description and experience. We address the issue of when experience-based judgments of probability are more accurate than are those based on description. If description is well understood ("transparent") and experience is misleading ("wicked"), it is preferable to experience. However, if description is not transparent, will valid ("kind") experience lead to more accurate judgments? We report 2 experiments. The first involved 7 well-known probabilistic inference tasks. Participants differed in statistical sophistication and answered with and without experience obtained through sequentially simulated outcomes. The second experiment involved interpreting the outcomes of a regression analysis when making inferences for investment decisions. In both experiments, even the statistically naïve achieved accurate probabilistic inferences after experiencing sequentially simulated outcomes, and many preferred this presentation format. We conclude by discussing theoretical and practical implications.

[1]  Thomas A. Keenan Computers and education , 1964, CACM.

[2]  Klaus Fiedler,et al.  Information Sampling and Adaptive Cognition , 2005 .

[3]  Robin M. Hogarth,et al.  Econometrics and decision making: Effects of presentation mode * , 2010 .

[4]  Peter Sedlmeier,et al.  How to improve statistical thinking: Choose the task representation wisely and learn by doing , 2000 .

[5]  L. Real Paradox, Performance, and the Architecture of Decision-Making in Animals' , 1996 .

[6]  Peter Sedlmeier,et al.  Improving Statistical Reasoning: Theoretical Models and Practical Implications , 1999 .

[7]  A. Tversky,et al.  Rational choice and the framing of decisions , 1990 .

[8]  Barry Sopher,et al.  Social Learning and Coordination Conventions in Intergenerational Games: An Experimental Study , 2003, Journal of Political Economy.

[9]  R. Fildes Journal of business: Lupoletti, William M. and Roy H. Webb, 1986, Defining and improving the accuracy of macroeconomic forecasts; contributions from a VAR model, 59, 263-284 , 1988 .

[10]  Gary L. Brase,et al.  Frequency interpretation of ambiguous statistical information facilitates Bayesian reasoning , 2008, Psychonomic bulletin & review.

[11]  Tomás Lejarraga When experience is better than description: Time delays and complexity , 2010 .

[12]  Robert S. Lockhart,et al.  Distributional versus singular approaches to probability and errors in probabilistic reasoning , 1993 .

[13]  Tilmann Betsch,et al.  Natural sampling and base-rate neglect , 1997 .

[14]  Craig R. Fox,et al.  “Decisions from experience” = sampling error + prospect theory: Reconsidering Hertwig, Barron, Weber & Erev (2004) , 2006, Judgment and Decision Making.

[15]  R. Hogarth,et al.  BEHAVIORAL DECISION THEORY: PROCESSES OF JUDGMENT AND CHOICE , 1981 .

[16]  Pedro L. Cobos,et al.  An associative framework for probability judgment: an application to biases. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[17]  G. Gigerenzer,et al.  Teaching Bayesian reasoning in less than two hours. , 2001, Journal of experimental psychology. General.

[18]  L. Beach,et al.  Experience and the base-rate fallacy. , 1982, Organizational behavior and human performance.

[19]  Lynn Hasher,et al.  Frequency processing: A twenty-five year perspective. , 2002 .

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

[21]  K. Fiedler,et al.  A sampling approach to biases in conditional probability judgments: beyond base rate neglect and statistical format. , 2000, Journal of experimental psychology. General.

[22]  N. Sanders,et al.  Journal of behavioral decision making: "The need for contextual and technical knowledge in judgmental forecasting", 5 (1992) 39-52 , 1992 .

[23]  Tilmann Betsch,et al.  Etc. frequency processing and cognition , 2002 .

[24]  D. Shanks On Similarities between Causal Judgments in Experienced and Described Situations , 1991 .

[25]  Håkan Nilsson,et al.  Exploring the conjunction fallacy within a category learning framework , 2008 .

[26]  Peter Sedlmeier,et al.  The distribution matters: two types of sample-size tasks , 1998 .

[27]  G Gigerenzer,et al.  Using natural frequencies to improve diagnostic inferences , 1998, Academic medicine : journal of the Association of American Medical Colleges.

[28]  Klaus Fiedler,et al.  Lottery attractiveness and presentation mode of probability and value information , 2011 .

[29]  L. Cosmides,et al.  Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty , 1996, Cognition.

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

[31]  A. Tversky,et al.  Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment , 1983 .

[32]  Lisa M. Schwartz,et al.  PSYCHOLOGICAL SCIENCE IN THE PUBLIC INTEREST Helping Doctors and Patients Make Sense of Health Statistics , 2022 .

[33]  K. Fiedler The dependence of the conjunction fallacy on subtle linguistic factors , 1988 .

[34]  Ivo D. Dinov,et al.  Pedagogical utilization and assessment of the statistic online computational resource in introductory probability and statistics courses , 2008, Comput. Educ..

[35]  Martin Weber,et al.  The Role of Experience Sampling and Graphical Displays on One's Investment Risk Appetite , 2012, Manag. Sci..

[36]  S. Krauss,et al.  The psychology of the Monty Hall problem: discovering psychological mechanisms for solving a tenacious brain teaser. , 2003, Journal of experimental psychology. General.