Ignoring information in binary choice with continuous variables: When is less “more”?

Hogarth, Robin M.; Karelaia, Natalia

doi:10.1016/j.jmp.2005.01.001

Cited by 107 publications

(83 citation statements)

References 10 publications

(11 reference statements)

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“…Generalizing from the above, this probability can be calculated using properties of the multivariate normal distribution which, in this case, can be written, Appendix B we derive the analytical expression for the probability of selecting the optimal choice among four alternatives by using just one variable. For binary choice, that is, when m = 2, analogous derivations lead to similar expressions to those shown above (see Hogarth & Karelaia, 2005).…”

Section: Models With Continuous Variablessupporting

confidence: 53%

“…Hogarth and Karelaia (2004;in press) and Baucells, Carrasco, and Hogarth (2005) have examined why TTB and other simple models perform well with binary attributes in error-free environments. And, Hogarth and Karelaia (2005) provided a theoretical analysis for the special case of binary choice with continuous attributes.…”

Section: Evidence On the Predictive Effectiveness Of Simple Modelsmentioning

confidence: 99%

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Regions of Rationality: Maps for Bounded Agents

Hogarth

Karelaia

2006

Decision Analysis

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An important problem faced by boundedly rational agents is to identify "regions of rationality," i.e., the areas for which simple, boundedly rational models are and are not effective. To map the contours of such regions, we derive probabilities that models identify the best of m alternatives (m > 2) characterized by k attributes (k > 1). The models include a single variable (lexicographic), variations of elimination-by-aspects, equal weighting, hybrids of the preceding, and models exploiting dominance. We compare all with multiple regression. We illustrate the theory with twenty simulated and four empirical datasets. Fits between predictions and realizations are excellent. However, the terrain mapped by our work is complex and no single model is "best." We further provide an overview by regressing the performance of the different models on factors characterizing environments. We conclude with suggestions for further research as well as implications for the concept of rationality in economics.

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Section: Models With Continuous Variablessupporting

confidence: 53%

Section: Evidence On the Predictive Effectiveness Of Simple Modelsmentioning

confidence: 99%

Regions of Rationality: Maps for Bounded Agents

Hogarth

Karelaia

2006

Decision Analysis

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Section: Europe Pmc Funders Author Manuscriptsmentioning

confidence: 99%

“…These results suggest that summing is not always necessary for good reasoning. In addition, some of the environmental structures under which weighting (ordering) without summing is ecologically rational have been identified (Hogarth & Karelaia, 2005;Martignon & Hoffrage, 2002; Payne et al, 1993).Here is the question that concerns us. If, as the work just reviewed demonstrates, both summing without weighting and weighting without summing can be as accurate as weighting and summing, why should humans not use these simpler heuristics?…”

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confidence: 99%

The Priority Heuristic: Making Choices Without Trade-offs

Brandstätter¹,

Gigerenzer²,

Hertwig³

2011

Heuristics

100

182

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Bernoulli's framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, we generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (i) Allais' paradox, (ii) risk aversion for gains if probabilities are high, (iii) risk seeking for gains if probabilities are low (lottery tickets), (iv) risk aversion for losses if probabilities are low (buying insurance), (v) risk seeking for losses if probabilities are high, (vi) certainty effect, (vii) possibility effect, and (viii) intransitivities. We test how accurately the heuristic predicts people's choices, compared to previously proposed heuristics and three modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attention-exchange model, and cumulative prospect theory.Conventional wisdom tells us that making decisions becomes difficult whenever multiple priorities, appetites, goals, values, or simply the attributes of the alternative options are in conflict. Should one undergo a medical treatment that has some chance of curing a lifethreatening illness but comes with the risk of debilitating side effects? Should one report a crime committed by a friend? Should one buy an expensive, high-quality camera or an inexpensive, low-quality camera? How do people resolve conflicts, ranging from the prosaic to the profound?The common denominator of many theories of human behavior is the premise that conflicts are mastered by making trade-offs. Since the Enlightenment, it has been believed that weighting and summing are the processes by which such trade-offs can be made in a rational way. Numerous theories of human behavior-including expected value theory, expected utility theory, prospect theory, Benjamin Franklin's moral algebra, theories of moral sense such as utilitarianism and consequentionalism (Gigerenzer, 2004), theories of risk taking (e.g., Wigfield & Eccles, 1992), motivational theories of achievement (Atkinson, 1957) and work behavior (e.g., Vroom, 1964), theories of social learning (Rotter, 1954), theories of attitude formation (e.g., Fishbein & Ajzen, 1975), and theories of health behavior (e.g., Becker, 1974; for a review see Heckhausen, 1991)-rest on these two processes. Take how expected utility theory would account for the choice between two investment plans as an example. The reasons for choosing are often negatively correlated with one another. High returns go with low probabilities, and low returns go with high probabilities. According to a common argument, negative correlations between reasons cause people to experience conflict, leading them to make trade-offs (Shanteau & Thomas, 2000). Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts utility, the trade-off between investment plans is performed by weighting the utility of the respective monetary outcomes by their probabilities and...

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“…Empirical support for the use of TTB in human populations comes from a variety of sources : Bröder & Schiffer, 2003b;Bergert & Nosofsky, 2007;Bröder & Gaissmaier, 2007;Nosofsky & Bergert, 2007;Rieskamp & Otto, 2006). Theoretical research provides characterizations of the statistical features of decision-making environments in which TTB can be successfully applied (Gigerenzer & Brighton, 2009;Hogarth & Karelaia, 2005a, 2005bBaucells, Carrasco, & Hogarth, 2008, Martignon & Hoffrage, 2002.…”

Section: Introductionmentioning

confidence: 99%

Fast and frugal heuristics in high-stakes investment decisions: New descriptive models of bank customers' behavior

Monti¹,

Martignon²,

Gigerenzer³

et al. 2009

PsycEXTRA Dataset

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This paper aim to uncover the decision processes used by average investors, including their investment goals, the information sets they consider, and the number of factors that actually influence high-stakes financial decisions. We present new experimental and survey data collected from bank customers at several Italian banks. Most subjects use a strict subset of the information available to them, ignoring variables that standard economic models typically assume drive investors' behavior. Fast and information-frugal heuristics appear to explain the information search and decision behavior of many subjects observed in this study, reflecting a lexicographic hierarchy of risk, time horizon and cost, in that order. A simple combination of a fast and frugal tree and a tallying rule predicts more than 80% of investors' decisions.

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Ignoring information in binary choice with continuous variables: When is less “more”?

Cited by 107 publications

References 10 publications

Regions of Rationality: Maps for Bounded Agents

Regions of Rationality: Maps for Bounded Agents

The Priority Heuristic: Making Choices Without Trade-offs

Fast and frugal heuristics in high-stakes investment decisions: New descriptive models of bank customers' behavior

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