Embracing Uncertainty: The Interface of Bayesian Statistics and Cognitive Psychology

Ecologists working in conservation and resource management are discovering the importance of using Bayesian analytic methods to deal explicitly with uncertainty in data analyses and decision making. However, Bayesian procedures require, as inputs and outputs, an idea that is problematic for the human brain: the probability of a hypothesis ("single−event probability"). I describe several cognitive concepts closely related to single−event probabilities, and discuss how their interchangeability in the human mind results in "cognitive illusions," apparent deficits in reasoning about uncertainty. Each cognitive illusion implies specific possible pitfalls for the use of single−event probabilities in ecology and resource management. I then discuss recent research in cognitive psychology showing that simple tactics of communication, suggested by an evolutionary perspective on human cognition, help people to process uncertain information more effectively as they read and talk about probabilities. In addition, I suggest that carefully considered standards for methodology and conventions for presentation may also make Bayesian analyses easier to understand.

[1]  Lyn Kathlene,et al.  Improving data utilization: the case-wise alternative , 1987 .

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

[3]  Donald Ludwig,et al.  Uncertainty and the Assessment of Extinction Probabilities , 1996 .

[4]  Deborah G. Mayo,et al.  Error and the Growth of Experimental Knowledge , 1996 .

[5]  L. Bondesson,et al.  A method to determine optimal stand data acquisition policies , 1994 .

[6]  Gerd Gigerenzer,et al.  The "conjunction fallacy" revisited : How intelligent inferences look like reasoning errors , 1999 .

[7]  P. Richerson,et al.  Culture and the Evolutionary Process , 1988 .

[8]  Guy Garrod,et al.  The non-use benefits of enhancing forest biodiversity: A contingent ranking study , 1997 .

[9]  Willem A. Wagenaar,et al.  Violation of utility theory in unique and repeated gambles , 1987 .

[10]  R. Hogarth,et al.  Shattering the Illusion of Control: Multi-shot Versus Single-Shot Gambles , 1994 .

[11]  Steve E. Hrudey,et al.  Mixed Messages in Risk Communication , 1997 .

[12]  J. Swets,et al.  A decision-making theory of visual detection. , 1954, Psychological review.

[13]  Gideon Keren,et al.  Additional tests of utility theory under unique and repeated conditions , 1991 .

[14]  Karl Halvor Teigen,et al.  When are low-probability events judged to be ‘probable’? Effects of outcome-set characteristics on verbal probability estimates , 1988 .

[15]  R. Peterman Statistical Power Analysis can Improve Fisheries Research and Management , 1990 .

[16]  G. Gigerenzer From Tools to Theories: A Heuristic of Discovery in Cognitive Psychology. , 1991 .

[17]  David Vose,et al.  Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling , 1996 .

[18]  Aaron M. Ellison,et al.  AN INTRODUCTION TO BAYESIAN INFERENCE FOR ECOLOGICAL RESEARCH AND ENVIRONMENTAL , 1996 .

[19]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[20]  Peter Urbach,et al.  Scientific Reasoning: The Bayesian Approach , 1989 .

[21]  Michael Siegrist,et al.  Communicating Low Risk Magnitudes: Incidence Rates Expressed as Frequency Versus Rates Expressed as Probability , 1997 .

[22]  Tim W. Clark,et al.  Creating and Using Knowledge for Species and Ecosystem Conservation: Science, Organizations, And Policy , 2015 .

[23]  Carl J. Walters,et al.  Adaptive Management of Renewable Resources , 1986 .

[24]  Carl J. Walters,et al.  Calculation of Bayes Posterior Probability Distributions for Key Population Parameters , 1994 .

[25]  Meaghan Morris,et al.  The truth is out there! , 2017, Psych-Talk.

[26]  T. Fearn,et al.  Bayesian statistics : principles, models, and applications , 1990 .

[27]  Jack L. Knetsch,et al.  Environmental policy implications of disparities between willingness to pay and compensation demanded measures of values , 1990 .

[28]  Murdoch K. McAllister,et al.  A Bayesian estimation and decision analysis for an age-structured model using biomass survey data , 1994 .

[29]  Mitchell J. Small,et al.  Bayesian Environmental Policy Decisions: Two Case Studies , 1996 .

[30]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[31]  Lynn A. Maguire,et al.  Resolving Environmental Disputes: a Framework Incorporating Decision Analysis and Dispute Resolution Techniques , 1994 .

[32]  Stephen Jay Gould Bully for brontosaurus : further reflections in natural history , 1992 .

[33]  Mervyn Thomas,et al.  A Novel Bayesian Approach to Assessing Impacts of Rain Forest Logging , 1996 .

[34]  Reid Hastie,et al.  Revision of beliefs when a hypothesis is eliminated from consideration. , 1985 .

[35]  E. Langer The illusion of control. , 1975 .

[36]  William R. Ferrell,et al.  Discrete subjective probabilities and decision analysis: Elicitation, calibration and combination. , 1994 .

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

[38]  S. Dehaene,et al.  The Number Sense: How the Mind Creates Mathematics. , 1998 .

[39]  Jane M. Packard,et al.  Acceptance of scientific management by natural resource dependent communities , 1997 .

[40]  Howard Raiffa,et al.  Decision analysis: introductory lectures on choices under uncertainty. 1968. , 1969, M.D.Computing.

[41]  G. Gigerenzer,et al.  Probabilistic mental models: a Brunswikian theory of confidence. , 1991, Psychological review.

[42]  Gary L. Brase,et al.  Individuation, counting, and statistical inference: The role of frequency and whole-object representations in judgment under uncertainty , 1998 .

[43]  P. Burton,et al.  The value of managing for biodiversity , 1992 .

[44]  Don Edwards,et al.  Comment: The First Data Analysis Should be Journalistic , 1996 .

[45]  L. Hasher,et al.  Automatic and effortful processes in memory. , 1979 .

[46]  Ronald D. Brunner,et al.  A Practice‐based Approach to Ecosystem Management , 1997 .

[47]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[48]  Peter G. Fairweather,et al.  Statistical Power and Design Requirements for Environmental Monitoring , 1991 .

[49]  P. R. Wade,et al.  A Bayesian Approach to Classification Criteria for Spectacled Eiders , 1996 .

[50]  David F. Parkhurst Decision analysis for toxic waste releases , 1984 .

[51]  Brian Dennis,et al.  Discussion: Should Ecologists Become Bayesians? , 1996 .

[52]  Gerd Gigerenzer,et al.  How to Improve Bayesian Reasoning Without Instruction: Frequency Formats , 1995 .

[53]  A. J. Underwood,et al.  Experiments in ecology and management: Their logics, functions and interpretations , 1990 .

[54]  G. Gigerenzer,et al.  Cognition as Intuitive Statistics , 1987 .

[55]  G. Gigerenzer,et al.  Do studies of statistical power have an effect on the power of studies , 1989 .

[56]  M. Granger Morgan,et al.  Graphical Communication of Uncertain Quantities to Nontechnical People , 1987 .

[57]  Randall M. Peterman,et al.  Results of Bayesian methods depend on details of implementation: an example of estimating salmon escapement goals , 1996 .

[58]  L. Joseph,et al.  Bayesian Statistics: An Introduction , 1989 .

[59]  Ray Hilborn,et al.  Conservation of harvested populations in fluctuating environments: the case of the Serengeti wildebeest , 1995 .

[60]  M. Feldman,et al.  Cultural transmission and evolution: a quantitative approach. , 1981, Monographs in population biology.

[61]  Shadish,et al.  Psychology of science: The perception and evaluation of quality in science , 1989 .

[62]  L. Hedges,et al.  The Handbook of Research Synthesis , 1995 .