CiteULike: voiklis Gigerenzer

The priority heuristic: Making choices without trade-offs

Eduard Brandstätter — 2007-09-19T02:28:44-00:00

Psychological Review, Vol. 113, No. 2. (2006), pp. 409-32.

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, the authors generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (a) the Allais paradox, (b) risk aversion for gains if probabilities are high, (c) risk seeking for gains if probabilities are low (e.g., lottery tickets), (d) risk aversion for losses if probabilities are low (e.g., buying insurance), (e) risk seeking for losses if probabilities are high, (f) the certainty effect, (g) the possibility effect, and (h) intransitivities. The authors test how accurately the heuristic predicts people's choices, compared with previously proposed heuristics and 3 modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attention-exchange model, and cumulative prospect theory.

Précis of Simple heuristics that make us smart

Peter Todd — 2007-09-19T02:12:37-00:00

Behavioral and Brain Sciences, Vol. 23, No. 05. (2001), pp. 727-741.

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we explore fast and frugal heuristics--simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.

How to learn good cue orders: When social learning benefits simple heuristics

Rocio García-Retamero — 2007-09-19T01:43:03-00:00

(2006), pp. 1352-1358.

Take The Best (TTB) is a simple one-reason decision-making strategy that searches through cues in the order of cue validities. Interestingly, this heuristic performs comparably to, or even better than, more complex information-demanding strategies such as multiple regression. The question of how a cue ordering is learned, however, has been only recently addressed by Dieckmann and Todd (2004). Surprisingly, these authors showed that learning cue orders through feedback--by updating cue validities--leads to a slow convergence to the ecological cue validities. Various other simple learning algorithms do not provide good results either. In the present paper, we provide a solution to this problem. Specifically, in a series of computer simulations, we show that simple social rules such as "imitate the successful" help to overcome the limitations of individual learning reported by Dieckmann and Todd (2004). Thus, the dilemma of individual learning can be collectively solved. In line with the spirit of bounded rationality, we found that several simple social rules performed comparably to, or better than computationally demanding social rules. We relate our results to previous findings on bounded rationality in the social context.

Environments That Make Us Smart: Ecological Rationality

Peter Todd — 2007-06-16T13:12:40-00:00

Current Directions in Psychological Science, Vol. 16, No. 3. (June 2007), pp. 167-171.

Traditional views of rationality posit general-purpose decision mechanisms based on logic or optimization. The study of ecological rationality focuses on uncovering the "adaptive toolbox" of domain-specific simple heuristics that real, computationally bounded minds employ, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.

Reasoning the fast and frugal way: Models of bounded rationality

Gerd Gigerenzer — 2007-09-17T17:01:00-00:00

Psychological Review, Vol. 103, No. 4. (1996), pp. 650-669.

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures (e.g., multiple regression). The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference.