Beyond Bayes's theorem: Effect of base-rate information in consensus games.

Abstract

Many of the practical implications of behavioral decision-making research are based on the assumption that behavioral trends have to be compared with normative prescriptions. The present article demonstrates that in certain settings this approach is both inapplicable because there is no "normative" prescription and unnecessary because robust quantitative predictions can be made without reference to normative prescriptions. Experiment 1 demonstrates that a simple learning rule can be used to predict the base-rate effect in consensus games with multiple equilibria. Experiment 2 shows that information about the payoff rule affects participants' initial propensities but does not affect the learning process. Some implications of these results for the understanding of decision groups in social contexts, such as employment decisions in organizations, are pointed out. Most attempts to derive practical implications from research on judgment and decision making are based on comparisons of observed descriptive tendencies to normative prescriptions of rational decision theory. Much of this research focuses on a debiasing technique that reduces the difference between observed decisions and the optimal prescription (e.g., see Von Winterfeldt & Edwards, 1986). Whereas this normative-based approach has led to useful results, its applicability is

Signal detection by human observers: A cutoff reinforcement learning model of categorization decisions under uncertainty.

“…Under this model, the probability of each response given a specific signal is determined by (matched to) the probability that this response will be correct (given the signal). Representative findings (later replicated by Gilat et al, 1997;Kubovy & Healy, 1997;and Ward, 1973) were obtained in Lee and Janke' s (1964) study. In this study, participants were asked to categorize stimuli (numbers, dot positions on a file card, or grayness of squares) that were samples from two normal distributions.…”

“…v We refer to this revised signal detection model as the cutoff reinforcement learning (CRL) signal detection model. Erev et al's results and the more recent findings of Gilat, Meyer, Erev, and Gopher (1997) and Gopher, Itkin, Erev, Meyer, and Armony ( 1995;summarized in Erev & Gopher, in press) appear to support this model. At least in the context of two-person signal detection tasks (studied in that research), the CRL signal detection model provides a good approximation of behavior.…”

“…Note that these trends cannot be explained by Lee's micromatching model, which implies no experience effect on SCV or estimated d'. Figure 3 presents the estimated d' in two control conditions that were run in the studies of Gilat et al (1997) and Gopher et al (1995). In both cases, the administrated d' was 1.5.…”

On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization

“…Second, to determine whether there might be increased variability for objective classifier feedback in addition to greater weight being placed on accuracy, we compared the hybrid(w Objective/d¢=1.0 ; w Optimal/d¢=1.0 ; w Objective/d ¢=2.2 ; w Optimal/d ¢=2.2 ) model with four accuracy weights and two criterion variability parameters with a more general model that assumed the same four accuracy weights and four criterion variability parameters (d¢ = 1.0/objective, d¢ = 1.0/optimal, d¢ = 2.2/objective, d¢ = 2.2/optimal). For 5 of 8 observers, the latter model provided a statistically significant improvement in fit (based on G 2 tests), providing some support for the notion that objective classifier feedback leads to greater variability in criterion placement, in addition to a reduction in the weight placed on accuracy (for related work from the decisionmaking literature see Barkan, 2002;Barkan, Zohar, & Erev, 1998;Gilat, Meyer, Erev, & Gopher, 1997).…”

Emergence of terminological conventions as a searcher–indexer coordination game

“…I do not have specific empirical evidence that play will converge in this case, but nominally, on the basis of published results in games with comparable structural complexity (e.g., . Gilat, Meyer, Erev, & Gopher 1997), I predict that:…”