Spectacular pehnomena and limits to rationality in genetic and cultural evolution

In studies of both animal and human behaviour, game theory is used as a tool for understanding strategies that appear in interactions between individuals. Game theory focuses on adaptive behaviour, which can be attained only at evolutionary equilibrium. Here we suggest that behaviour appearing during interactions is often outside the scope of such analysis. In many types of interaction, conflicts of interest exist between players, fueling the evolution of manipulative strategies. Such strategies evolve out of equilibrium, commonly appearing as spectacular morphology or behaviour with obscure meaning, to which other players may react in non-adaptive, irrational way approach, and outline the conditions in which evolutionary equilibria cannot be maintained. Evidence from studies of biological interactions seems to support the view that behaviour is often not at equilibrium. This also appears to be the case for many human cultural traits, which have spread rapidly despite the fact that they have a negative influence on reproduction

Information Aggregation Under Ambiguity: Theory and Experimental Evidence

We study information aggregation in a dynamic trading model with partially informed and ambiguity averse traders. We show theoretically that separable securities, introduced by Ostrovsky (2012) in the context of Subjective Expected Utility, no longer aggregate information if some traders have imprecise beliefs and are ambiguity averse. Moreover, these securities are prone to manipulation, as the degree of information aggregation can be influenced by the initial price, set by the uninformed market maker. These observations are also confirmed in our experiment, using prediction markets. We define a new class of strongly separable securities which are robust to the above considerations, and show that they characterize information aggregation in both strategic and non-strategic environments. We derive several theoretical predictions, which we are able to confirm in the lab

Decision making under time pressure: an independent test of sequential sampling models

Choice probability and choice response time data from a risk-taking decision-making task were compared with predictions made by a sequential sampling model. The behavioral data, consistent with the model, showed that participants were less likely to take an action as risk levels increased, and that time pressure did not have a uniform effect on choice probability. Under time pressure, participants were more conservative at the lower risk levels but were more prone to take risks at the higher levels of risk. This crossover interaction reflected a reduction of the threshold within a single decision strategy rather than a switching of decision strategies. Response time data, as predicted by the model, showed that participants took more time to make decisions at the moderate risk levels and that time pressure reduced response time across all risk levels, but particularly at the those risk levels that took longer time with no pressure. Finally, response time data were used to rule out the hypothesis that time pressure effects could be explained by a fast-guess strategy

Do People Make Decisions Under Risk Based on Ignorance? An Empirical Test of the Priority Heuristic against Cumulative Prospect Theory

Brandstätter, Gigerenzer and Hertwig (2006) put forward the priority heuristic (PH) as a fast and frugal heuristic for decisions under risk. According to the PH, individuals do not make trade-offs between gains and probabilities, as proposed by expected utility models such as cumulative prospect theory (CPT), but use information in a non-compensatory manner and ignore information. We conducted three studies to test the PH empirically by analyzing individual choice patterns, decision times and information search parameters in diagnostic decision tasks. Results on all three dependent variables conflict with the predictions of the PH and can be better explained by the CPT. The predictive accuracy of the PH was high for decision tasks in which the predic-tions align with the predictions of the CPT but very low for decision tasks in which this was not the case. The findings indicate that earlier results supporting the PH might have been caused by the selection of decision tasks that were not diagnostic for the PH as compared to CPT.Decision Strategy, Fast and Frugal Heuristics, Bounded Rationality, Decision Latency, Process Tracing, Cumulative Prospect Theory

Non-Bayesian Testing of a Stochastic Prediction

We propose a method to test a prediction of the distribution of a stochastic process. In a non-Bayesian non-parametric setting, a predicted distribution is tested using a realization of the stochastic process. A test associates a set of realizations for each predicted distribution, on which the prediction passes. So that there are no type I errors, a prediction assigns probability 1 to its test set. Nevertheless, these sets are small, in the sense that "most" distributions assign it probability 0, and hence there are few type II errors. It is also shown that there exists such a test that cannot be manipulated, in the sense that an uninformed predictor who is pretending to know the true distribution is guaranteed to fail on an uncountable number of realizations, no matter what randomized prediction he employs. The notion of a small set we use is category I, described in more detail in the paper.

Forecasting the fast and frugal way: A study of performance and information-processing strategies of experts and non-experts when predicting the World Cup 2002 in soccer

This paper investigates forecasting performance and judgmental processes of experts and non-experts in soccer. Two circumstances motivated the paper: (i) little is known about how accurately experts predict sports events, and (ii) recent research on human judgment suggests that ignorance-based decision-strategies may be reliable. About 250 participants with different levels of knowledge of soccer took part in a survey and predicted the outcome of the first round of World Cup 2002. It was found that the participating experts (i.e., sport journalists, soccer fans, and soccer coaches) were not more accurate than the non-experts. Experts overestimated their performance and were overconfident. While the experts claimed to have relied on analytical approaches and much information, participants with limited knowledge stated that their forecasts were based upon recognition and few pieces of information. The paper concludes that a recognition-based strategy seems to be appropriate when forecasting worldwide soccer events.Expert predictions; Information use; Judgmental forecasting; Overconfidence; Recognition heuristic; Sports forecasting

Challenging the role of implicit processes in probabilistic category learning

Considerable interest in the hypothesis that different cognitive tasks recruit qualitatively distinct processing systems has led to the proposal of separate explicit (declarative) and implicit (procedural) systems. A popular probabilistic category learning task known as the weather prediction task is said to be ideally suited to examine this distinction because its two versions, '' observation '' and '' feedback,'' are claimed to recruit the declarative and procedural systems, respectively. In two experiments, we found results that were inconsistent with this interpretation. In Experiment 1, a concurrent memory task had a detrimental effect on the implicit (feedback) version of the task. In Experiment 2, participants displayed comparable and accurate insight into the task and their judgment processes in the feedback and observation versions. These findings have important implications for the study of probabilistic category learning in both normal and patient populations

The role of decision confidence in advice-taking and trust formation

In a world where ideas flow freely between people across multiple platforms, we often find ourselves relying on others' information without an objective standard to judge whether those opinions are accurate. The present study tests an agreement-in-confidence hypothesis of advice perception, which holds that internal metacognitive evaluations of decision confidence play an important functional role in the perception and use of social information, such as peers' advice. We propose that confidence can be used, computationally, to estimate advisors' trustworthiness and advice reliability. Specifically, these processes are hypothesized to be particularly important in situations where objective feedback is absent or difficult to acquire. Here, we use a judge-advisor system paradigm to precisely manipulate the profiles of virtual advisors whose opinions are provided to participants performing a perceptual decision making task. We find that when advisors' and participants' judgments are independent, people are able to discriminate subtle advice features, like confidence calibration, whether or not objective feedback is available. However, when observers' judgments (and judgment errors) are correlated - as is the case in many social contexts - predictable distortions can be observed between feedback and feedback-free scenarios. A simple model of advice reliability estimation, endowed with metacognitive insight, is able to explain key patterns of results observed in the human data. We use agent-based modeling to explore implications of these individual-level decision strategies for network-level patterns of trust and belief formation