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. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)
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.
When searching for concepts in memory—as in the verbal fluency task of naming all the animals one can think of—people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of the patches being searched in mental space is not well specified. Do we search by activating explicit predefined categories and recall items from within that category, or do we (...) activate and recall a connected sequence of individual items without using categorical information, with each item recalled leading to the retrieval of an associated item in a stream, or both? Using semantic representations in a search of associative memory framework and data from the animal fluency task, we tested competing hypotheses based on associative and categorical search models. Associative, but not categorical, patch transitions took longer to make than position-matched productions, suggesting that categorical transitions were not true transitions. There was also clear evidence of associative search even within categorical patch boundaries. Furthermore, most individuals' behavior was best explained by an associative search model without the addition of categorical information. Thus, our results support a search process that does not use categorical information, but for which patch boundaries shift with each recall and local search is well described by a random walk in semantic space, with switches to new regions of the semantic space when the current region is depleted. (shrink)
To understand the possible forms of extraterrestrial intelligence, we need not only astrobiology theories about how life evolves given habitable planets, but also evolutionary psychology theories about how intelligence emerges given life. Wherever intelligent organisms evolve, they are likely to face similar behavioral challenges in their physical and social worlds. The cognitive mechanisms that arise to meet these challenges may then be copied, repurposed, and shaped by further evolutionary selection to deal with more abstract, higher-level cognitive tasks such as conceptual (...) reasoning, symbolic communication, and technological innovation, while retaining traces of the earlier adaptations for solving physical and social problems. These traces of evolutionary pathways may be leveraged to gain insight into the likely cognitive processes of ETIs. We demonstrate such analysis in the domain of search strategies and show its application in the domains of emotional aversions and social/sexual signaling. Knowing the likely evolutionary pathways to intelligence will help us to better search for and process any alien signals from the search for ETIs and to assess the likely benefits, costs, and risks of humans actively messaging ETIs. (shrink)
While theories of rationality and decision making typically adopt either a single-powertool perspective or a bag-of-tricks mentality, the research program of ecological rationality bridges these with a theoretically-driven account of when different heuristic decision mechanisms will work well. Here we described two ways to study how heuristics match their ecological setting: The bottom-up approach starts with psychologically plausible building blocks that are combined to create simple heuristics that fit specific environments. The top-down approach starts from the statistical problem facing the (...) organism and a set of principles, such as the bias– variance tradeoff, that can explain when and why heuristics work in uncertain environments, and then shows how effective heuristics can be built by biasing and simplifying more complex models. We conclude with challenges these approaches face in developing a psychologically realistic perspective on human rationality. (shrink)
A working assumption that processes of natural and cultural evolution have tailored the mind to fit the demands and structure of its environment begs the question: how are we to characterize the structure of cognitive environments? Decision problems faced by real organisms are not like simple multiple-choice examination papers. For example, some individual problems may occur much more frequently than others, whilst some may carry much more weight than others. Such considerations are not taken into account when (i) the performance (...) of candidate cognitive mechanisms is assessed by employing a simple accuracy metric that is insensitive to the structure of the decision-maker's environment, and (ii) reason is defined as the adherence to internalist prescriptions of classical rationality. Here we explore the impact of frequency and significance structure on the performance of a range of candidate decision-making mechanisms. We show that the character of this impact is complex, since structured environments demand that decision-makers trade off general performance against performance on important subsets of test items. As a result, environment structure obviates internalist criteria of rationality. Failing to appreciate the role of environment structure in shaping cognition can lead to mischaracterising adaptive behavior as irrational. (shrink)
Shepard promotes the important view that evolution constructs cognitive mechanisms that work with internalized aspects of the structure of their environment. But what can this internalization mean? We contrast three views: Shepard's mirrors reflecting the world, Brunswik's lens inferring the world, and Simon 's scissors exploiting the world. We argue that Simon 's scissors metaphor is more appropriate for higher-order cognitive mechanisms and ask how far it can also be applied to perceptual tasks. [Barlow; Kubovy & Epstein; Shepard].
Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it (...) within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments. (shrink)
How can cooperation be achieved between self-interested individuals in commonly-occurring asymmetric interactions where agents have different positions? Should agents use the same strategies that are appropriate for symmetric social situations? We explore these questions through the asymmetric interaction captured in the indefinitely repeated investment game. In every period of this game, the first player decides how much of an endowment he wants to invest, then this amount is tripled and passed to the second player, who finally decides how much of (...) the tripled investment she wants to return to the first player. The results of three evolutionary studies demonstrate that the best-performing strategies for this asymmetric game differ from those for a similar but symmetric game, the indefinitely repeated Prisoner’s dilemma game. The strategies that enable cooperation for the asymmetric IG react more sensitively to exploitation, meaning that cooperation can more easily break down. Furthermore, once cooperation has stopped, it is much more difficult to reestablish than in symmetric situations. Based on these results, the presence of asymmetry in an interaction appears to be an important factor affecting adaptive behavior in these common social situations. (shrink)
Atran conjectures that a triggering algorithm for a living- kind module could involve inputs from other modules that detect animacy and intentionality. Here we further speculate about how algorithms for detecting specific intentions could be used to trigger between- or within-species categorization. Such categorization may be adaptively important in Eldredge's energy and information realms.
Evolutionary psychologists should go beyond research on individual differences in attitudes and focus more on detailed models of psychological mechanisms. We argue for complementing attitude research with agent-based computational modeling of mate choice. Agent-based models require detailed specification of individual choice mechanisms that can be evaluated in terms of both their psychological plausibility and the population-level outcomes they produce.
Search can be found in almost every cognitive activity, ranging across vision, memory retrieval, problem solving, decision making, foraging, and social interaction. Because of its ubiquity, research on search has a tendency to fragment into multiple areas of cognitive science. The proposed topic aims at providing integrative discussion of the central role of search from multiple perspectives. We focus on controlled search processes, which require a goal, uncertainty about the nature, location, or acquisition method of the objects to be searched (...) for, and a method for sampling through the search environment. While this definition of search is general and applicable to different domains, the specific mechanisms in the search process will likely differ. The goal of this issue is to compare and contrast how these search processes are similar and differ in different cognitive activities, with the goal of understanding the general nature of search in terms of the three characteristics stated above. We expect that given its cross-domain nature, the topic on search will be of broad interest to cognitive scientists, including psychologists, behavioral ecologists, computer scientists, neuroscientists, linguists, and sociologists. (shrink)