This chapter investigates the extent to which claims of massive modular organization of the mind (espoused by some members of the evolutionary psychology research program) are consistent with the main elements of the simple heuristics research program. A number of potential sources of conflict between the two programs are investigated and defused. However, the simple heuristics program turns out to undermine one of the main arguments offered in support of massive modularity, at least as the latter (...) is generally understood by philosophers. So one result of the argument will be to force us to re-examine the way in which the notion of modularity in cognitive science should best be characterized, if the thesis of massive modularity isn’t to be abandoned altogether. What is at stake in this discussion, is whether there is a well-motivated notion of ‘module’ such that we have good reason to think that the human mind must be massively modular in its organization. I shall be arguing (in the end) that there is. (shrink)

Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It (...) is about fast and frugal heuristics--simple rules for making decisions when time is pressing and deep thought an unaffordable luxury. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? Can judgments based simply on one good reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? Simple Heuristics explores these questions, developing computational models of heuristics and testing them through experiments and analyses. It shows how fast and frugal heuristics can produce adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop out rates, and playing the stock market. As an interdisciplinary work that is both useful and engaging, this book will appeal to a wide audience. It is ideal for researchers in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. It will also inspire anyone interested in simply making good decisions. (shrink)

This title invites readers to discover the simple heuristics that people use to navigate the complexities and surprises of environments populated with others.

Simple heuristics are clearly powerful tools for making near optimal decisions, but evidence for their use in specific situations is weak. Gigerenzer et al. (1999) suggest a range of heuristics, but fail to address the question of which environmental or task cues might prompt the use of any specific heuristic. This failure compromises the falsifiability of the fast and frugal approach.

The simple heuristics that may indeed usually make us smart–or at least smart enough–in contexts of individual choice will sometimes make us dumb, especially in contexts of social choice. Here each individual choice (or vote) has little impact on the overall choice, although the overall choice is compounded out of the individual choices. I use an example (risk aversion) to illustrate the point.

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)

The Adaptive Toolbox framework specifies heuristics for choice and categorisation that search through cues in previously learned orders (Gigerenzer & Todd, 1999). We examined the learning of three cue parameters defining different orders: discrimination rate (DR) (the probability that a cue points to a unique choice), validity (the probability of correct choice given that a cue discriminates), and success (the probability of correct choice). Success orderings are identical to those by expected information gain (Klayman & Ha, 1987). In two (...) experiments, participants made choices in real-world environments with objective outcome criteria. Participant ratings indicated some appropriate parameter learning when the relevant cue parameter values were highly dispersed. Rated orders of cue validity and DR were less distinct than the objective orders—learning one parameter may be biased towards success by variation in the other. Success ratings capture the variation in validity and DR as well as participants' perception of these parameters. (shrink)

Gigerenzer, Todd, and the ABC Research Group argue that optimisation under constraints leads to an infinite regress due to decisions about how much information to consider when deciding. In certain cases, however, their fast and frugal heuristics lead instead to an endless series of decisions about how best to decide.

This article provides an overview of recent results on lexicographic, linear, and Bayesian models for paired comparison from a cognitive psychology perspective. Within each class, we distinguish subclasses according to the computational complexity required for parameter setting. We identify the optimal model in each class, where optimality is defined with respect to performance when fitting known data. Although not optimal when fitting data, simple models can be astonishingly accurate when generalizing to new data. A simple heuristic belonging to (...) the class of lexicographic models is Take The Best (Gigerenzer & Goldstein (1996) Psychol. Rev. 102: 684). It is more robust than other lexicographic strategies which use complex procedures to establish a cue hierarchy. In fact, it is robust due to its simplicity, not despite it. Similarly, Take The Best looks up only a fraction of the information that linear and Bayesian models require; yet it achieves performance comparable to that of models which integrate information. Due to its simplicity, frugality, and accuracy, Take The Best is a plausible candidate for a psychological model in the tradition of bounded rationality. We review empirical evidence showing the descriptive validity of fast and frugal heuristics. (shrink)

In M. Werning, W. Hinzen, and E. Machery (Eds.), The Oxford Handbook of Compositionality.

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge‐based inference depending on the subjective (...) difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge‐based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge‐based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. (shrink)

To evaluate the success of simple heuristics we need to know more about how a relevant heuristic is chosen and how we learn which cues are relevant. These meta-abilities are at the core of ecological rationality, rather than the individual heuristics.

This commentary focuses on three issues raised by Gigerenzer, Todd, and the ABC Research Group (1999). First, I stress the need for further experimental evidence to determine which heuristics people use in cognitive judgment tasks. Second, I question the scope of cognitive models based on simple heuristics, arguing that many aspects of cognition are too sophisticated to be modeled in this way. Third, I note the complementary role that rational explanation can play to Gigenerenzer et al.'s “ecological” (...) analysis of why heuristics succeed. (shrink)

Humans are incapable of acting as utility maximisers. In contrast, the evolutionary process had considerable time and computational power to select optimal heuristics from a set of alternatives. To view evolution as the optimising agent has revolutionised game theory. Gigerenzer and his co-workers can help us understand the circumstances under which evolution and learning achieve optimisation and Nash equilibria.

Simple heuristics that make us smart presents a valuable and valid interpretation of how we make fast decisions particularly in situations of ignorance and uncertainty. What is missing is how this intersects with thinking under even greater uncertainty or ignorance, such as novice problem solving, and with the development of expert cognition.

Cimpian & Salomon's proposal comes unstuck on precisely the claim that inherence is an heuristic, able to deliver simple, shallow outputs that are right most of the time. Instead, the inherence heuristic delivers outputs that imply it is not an heuristic after all, and is simply too fast and too easy a mechanism to do the job of explaining categorisations.

The notion of ecological rationality implies that the accuracy of a decision strategy depends on features of the information environment in which it is tested. We demonstrate that the performance of a group may be strongly affected by the decision strategies used by its individual members and specify how this effect is moderated by environmental features. Specifically, in a set of simulation studies, we systematically compared four decision strategies used by the individual group members: two linear, compensatory decision strategies and (...) two simple, noncompensatory heuristics. Individual decisions were aggregated by using a majority rule. To assess the ecological rationality of the strategies, we varied (a) the distribution of cue validities, (b) the quantity, and (c) the quality of shared information. Group performance strongly depended on the distribution of cue validities. When validities were linearly distributed, groups using a compensatory strategy achieved the highest accuracy. Conversely, when cue validities followed a J-shaped distribution, groups using a simple lexicographic heuristic performed best. While these effects were robust across different quantities of shared information, the quality of shared information exerted stronger effects on group performance. Consequences for prescriptive theories on group decision making are discussed. (shrink)

I argue that an ecologically distributed conception of instrumental rationality can and should be extended to a socially distributed conception of instrumental rationality in social environments. The argument proceeds by showing that the assumption of exogenously fixed units of activity cannot be justified; different units of activity are possible and some are better means to independently given ends than others, in various circumstances. An important social heuristic, the mirror heuristic, enables the flexible formation of units of activity in game theoretic (...) situations, including collective units where these are instrumentally effective. In effect, the mirror heuristic makes the formation of units of activity endogenous to instrumental rationality. Moreover, the mirror heuristic is a conditional metaheuristic, which depends on mind reading of the heuristics of other players rather than on predictions of their behavior. Such mind reading can be regarded as emerging from an arms race between behavioral mimicry and ever smarter behavior reading. Even though unilateral mind reading may have benefits, the mirror metaheuristic illustrates that mutual mind reading has distinctive functions in responding to the challenges of social complexity. If simple heuristics can make us smart in the right environments, then social heuristics can make us smarter still. (shrink)

Simple heuristics that make us smart offers an impressive compilation of work that demonstrates fast and frugal (one-reason) heuristics can be simple, adaptive, and accurate. However, many decision environments differ from those explored in the book. We conducted a Monte Carlo simulation that shows one-reason strategies are accurate in “friendly” environments, but less accurate in “unfriendly” environments characterized by negative cue intercorrelations, that is, tradeoffs.

There are parallels between Gigerenzer et al.'s emphasis on the rationality of adults' reasoning in terms of simple heuristics and developmental researchers' emphasis on the rationality of children's reasoning in terms of intuitive theories. Indeed, just as children become better at using their theories, so might some people, experts, become better at using simple heuristics.

This review of Gigerenzer, Todd, and the ABC Research Group's Simple heuristics that make us smart focuses on the role of heuristics in discovery, invention, and hypothesis-testing and concludes with a comment on the role of heuristics in population growth.

This study aims to open a critical scenario on the current paradigm of reference for teaching sports techniques by pointing to a methodological perspective that is more in keeping with the theoretical reference framework of the ecological-dynamic approach. Currently, the Constraints-led Approach is considered very useful for teaching sports techniques; however, the transition from pure theory to practice is not so simple and consistent with the heuristic learning paradigm. The purpose is to highlight, starting from the theoretical lines, the (...) methodological applications of the different ways of applying the reference paradigm, analyzing the contrasts of the significant aspects and the uniqueness and unrepeatability of heuristic learning. For this purpose, an in-depth review of the scientific literature was conducted, highlighting the main critical points of the approach, also defined as nonlinear, with the consequent elaboration of operational proposals for the various macro-families of sports. (shrink)

A heuristic is a rule of thumb. In psychology, heuristics are relatively simple rules for making judgments. A fast heuristic is easy to use and allows one to make judgments quickly. A frugal heuristic relies on a small fraction of the available evidence in making judgments. Typically, fast and frugal heuristics (FFHs) have, or are claimed to have, a further property: They are very reliable, yielding judgments that are about as accurate in the long run as ideal (...) non-fast, non-frugal rules. This paper introduces some well-known examples of FFHs, raises some objections to the FFH program, and looks at the implications of those parts of the FFH program about which we can have some reasonable degree of confidence. (shrink)

Tense/aspect morphology on verbs is often thought to depend on event features like telicity, but it is not known how speakers identify these features in visual scenes. To examine this question, we asked Japanese speakers to describe computer‐generated animations of simple actions with variation in visual features related to telicity. Experiments with adults and children found that they could use goal information in the animations to select appropriate past and progressive verb forms. They also produced a large number of (...) different verb forms. To explain these findings, a deep‐learning model of verb production from visual input was created that could produce a human‐like distribution of verb forms. It was able to use visual cues to select appropriate tense/aspect morphology. The model predicted that video duration would be related to verb complexity, and past tense production would increase when it received the endpoint as input. These predictions were confirmed in a third study with Japanese adults. This work suggests that verb production could be tightly linked to visual heuristics that support the understanding of events. (shrink)

People don't always speak the truth. When they don't, we do better not to trust them. Unfortunately, that's often easier said than done. People don't usually wear a ‘Not to be trusted!’ badge on their sleeves, which lights up every time they depart from the truth. Given this, what can we do to figure out whom to trust, and whom not? My aim in this paper is to offer a partial answer to this question. I propose a heuristic—the “Humility Heuristic”—which (...) is meant to help guide our search for trustworthy advisors. In slogan form, the heuristic says: people worth trusting admit to what they don't know. I give this heuristic a precise probabilistic interpretation, offer a simple argument for it, defend it against some potential worries, and demonstrate its practical worth by showing how it can help address some difficult challenges in the relationship between experts and laypeople. (shrink)

Simple heuristics exploit basic human abilities, such as recognition memory, to make decisions based on sparse information. Based on the relative speed of recognizing two objects, the fluency heuristic infers that the one recognized more quickly has the higher value with respect to the criterion of interest. Behavioral data show that reliance on retrieval fluency enables quick inferences. Our goal with the present functional magnetic resonance imaging study was to isolate fluency-heuristic-based judgments to map the use of fluency (...) onto specific brain areas that might give a better understanding of the heuristic’s underlying processes. Activation within the claustrum for fluency heuristic decisions was found. Given that claustrum activation is thought to reflect the integration of perceptual and memory elements into a conscious gestalt, we suggest this activation correlates with the experience of fluency. (shrink)

The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they (...) `fit to reality.' The main purpose of this article is to apply these ideas to learning rules–-methods for constructing, selecting, or evaluating competing hypotheses in science, and to the methodology of machine learning, of which connectionist learning is a special case. The bad news is that ecological validity is particularly difficult to implement and difficult to understand. The good news is that it builds an important bridge from normative psychology and machine learning to recent work in the philosophy of science, which considers predictive accuracy to be a primary goal of science. (shrink)

Based on a rather simple thesis that we can learn from our mistakes, Karl Popper developed a falsificationist epistemology in which knowledge grows through falsifying, or criticizing, our theories. According to him, knowledge, especially scientific knowledge, progresses through conjectures that are controlled by criticism, or attempted refutations . As he puts it, ‘Criticism of our conjectures is of decisive importance: by bringing out our mistakes it makes us understand the difficulties of the problem which we are trying to solve. (...) This is how we become better acquainted with our problem, and able to propose more mature solutions: the very refutation of a theory ... is always a step forward that takes us nearer to the truth. And this is how we can learn from our mistakes’ . Since criticism plays such a crucial role in Popper's falsificationist methodology, it seems natural to envisage his heuristic as a helpful resource for developing critical thinking. However, there is much controversy in the psychological literature over the feasibility and utility of his falsificationism as a heuristic. In this paper, I first consider Popper's falsificationism within the framework of his critical rationalism, elucidating three core and interrelated concepts, viz. fallibilism, criticism, and verisimilitude. Then I argue that the implementation of Popper's falsificationism means exposing to criticism various philosophical presuppositions that work against criticism, such as essentialism, instrumentalism, and conventionalism; it also means combating what seems a common tendency of humans to be biased towards confirmation. I examine the confirmation bias, to which Popper did not give much attention: its pervasiveness and various guises, some theoretical explanations for it, and the role of teachers in undermining its strength and spread. Finally, I consider the question whether students can and should be taught to use disconfirmatory strategies for solving problems. (shrink)

Based on a rather simple thesis that we can learn from our mistakes, Karl Popper developed a falsificationist epistemology in which knowledge grows through falsifying, or criticizing, our theories. According to him, knowledge, especially scientific knowledge, progresses through conjectures (i.e. tentative solutions to problems) that are controlled by criticism, or attempted refutations (including severely critical tests). As he puts it, ‘Criticism of our conjectures is of decisive importance: by bringing out our mistakes it makes us understand the difficulties of (...) the problem which we are trying to solve. This is how we become better acquainted with our problem, and able to propose more mature solutions: the very refutation of a theory... is always a step forward that takes us nearer to the truth. And this is how we can learn from our mistakes’ (1989, p. vii). Since criticism plays such a crucial role in Popper's falsificationist methodology, it seems natural to envisage his heuristic as a helpful resource for developing critical thinking. However, there is much controversy in the psychological literature over the feasibility and utility of his falsificationism as a heuristic. In this paper, I first consider Popper's falsificationism within the framework of his critical rationalism, elucidating three core and interrelated concepts, viz. fallibilism, criticism, and verisimilitude. Then I argue that the implementation of Popper's falsificationism means exposing to criticism various philosophical presuppositions that work against criticism, such as essentialism, instrumentalism, and conventionalism; it also means combating what seems a common tendency of humans to be biased towards confirmation. I examine the confirmation bias, to which Popper did not give much attention: its pervasiveness and various guises, some theoretical explanations for it, and the role of teachers in undermining its strength and spread. Finally, I consider the question whether students can and should be taught to use disconfirmatory strategies for solving problems. (shrink)

Einstein located the foundations of general relativity in simple and vivid physical principles: the principle of equivalence, an extended principle of relativity and Mach's principle. While these ideas played an important heuristic role in Einstein's thinking, they provide a dubious logical foundation for his final theory. Einstein was also guided to his final theory, I argue, by a second tier of more prosaic heuristics. I trace one strand among them. The principle of equivalence guided Einstein well until it (...) led him to a theory that contradicted the conservation of momentum. Einstein converted the requirement of conservation of energy and momentum into a procedure that he used repeatedly for finding gravitational field equations. That procedure survives in present day developments of general relativity. (shrink)

What does 'if' mean? Timothy Williamson presents a controversial new approach to understanding conditional thinking, which is central to human cognitive life. He argues that in using 'if' we rely on psychological heuristics, fast and frugal methods which can lead us to trust faulty data and prematurely reject simple theories.

Our programmatic article on Homo heuristicus (Gigerenzer & Brighton, 2009) included a methodological section specifying three minimum criteria for testing heuristics: competitive tests, individual-level tests, and tests of adaptive selection of heuristics. Using Richter and Späth’s (2006) study on the recognition heuristic, we illustrated how violations of these criteria can lead to unsupported conclusions. In their comment, Hilbig and Richter conduct a reanalysis, but again without competitive testing. They neither test nor specify the compensatory model of inference they (...) argue for. Instead, they test whether participants use the recognition heuristic in an unrealistic 100% (or 96%) of cases, report that only some people exhibit this level of consistency, and conclude that most people would follow a compensatory strategy. We know of no model of judgment that predicts 96% correctly. The curious methodological practice of adopting an unrealistic measure of success to argue against a competing model, and to interpret such a finding as a triumph for a preferred but unspecified model, can only hinder progress. Marewski, Gaissmaier, Schooler, Goldstein, and Gigerenzer (2010), in contrast, specified five compensatory models, compared them with the recognition heuristic, and found that the recognition heuristic predicted inferences most accurately. (shrink)

Este artículo investiga la coherencia entre la propuesta de una organización modular masiva de la mente y el enfoque de las heurísticas simples. Se discute una serie de potenciales conflictos entre los dos programas, pero finalmente son desestimados. De todos modos, el programa de las heurísticas simples sí termina socavando uno de los muchos argumentos propuestos para apoyar la modularidad masiva, al menos en el modo en que esta última es comprendida por los filósofos. Así que un resultado de la (...) argumentación será forzarnos a reexaminar la mejor manera de caracterizar la noción de modularidad en la ciencia cognitiva, para que la tesis de la modularidad masiva no deba ser abandonada completamente. Lo que está en juego en esta discusión es si existe una noción de "módulo" que esté bien motivada, de modo que tengamos una buena razón para pensar que la mente humana deba ser masivamente modular en su organización. Argumentaré que tal noción existe. This chapter investigates the extent to which claims of massive modular organization of the mind are consistent with the main elements of the simple heuristics research program. A number of potential sources of conflict between the two programs are investigated and defused. However, the simple heuristics program turns out to undermine one of the main arguments offered in support of massive modularity, at least as the latter is generally understood by philosophers. So one result of the argument will be to force us to re-examine the way in which the notion of modularity in cognitive science should best be characterized, if the thesis of massive modularity isn't to be abandoned altogether. What is at stake in this discussion, is whether there is a well-motivated notion of "module" such that we have good reason to think that the human mind must be massively modular in its organization. I shall be arguing that there is. (shrink)

We study the performance of heuristics relative to the performance of optimal solutions in the rich domain of sequential search, where the decision to stop the search depends only on the applicant’s relative rank. Considering multiple variants of the secretary problem, that vary from one another in their formulation and method of solution, we find that descriptive heuristics perform well only when the optimal solution prescribes a single threshold value. We show that a computational heuristic originally proposed as (...) an approximate solution to a single variant of the secretary problem performs equally well in many other variants where the optimal solution prescribes multiple threshold values that gradually relax the criterion for stopping the search. Finally, we propose a new heuristic with near optimal performance in a competitive or strategic variant of the secretary problem with multiple employers competing with one another to hire job applicants. Both heuristics share a simple computational component: the ratio of the number of interviewed applicants to the number of those remaining to be searched. We present the subgame-perfect Nash equilibrium for this competitive variant and an algorithm for its computation. (shrink)

This article proposes a novel integrative approach to moral judgment and a related model that could explain how unconscious heuristic processes are transformed into consciously accessible moral intuitions. Different hypothetical cases have been tested empirically to evoke moral intuitions that support principles from competing moral theories. We define and analyze the types of intuitions that moral theories and studies capture: those focusing on agents (A), deeds (D), and consequences (C). The integrative ADC approach uses the heuristic principle of “attribute substitution” (...) to explain how people make intuitive judgments. The target attributes of moral judgments are moral blameworthiness and praiseworthiness, which are substituted with more accessible and computable information about an agent's virtues and vices, right/wrong deeds, and good/bad consequences. The processes computing this information are unconscious and inaccessible, and therefore explaining how they provide input for moral intuitions is a key problem. We analyze social heuristics identified in the literature and offer an outline for a new model of moral judgment. Simple social heuristics triggered by morally salient cues rely on three distinct processes (role-model entity, action analysis, and consequence tallying—REACT) in order to compute the moral valence of specific intuitive responses (A, D, and C). These are then rapidly combined to form an intuitive judgment that could guide quick decision making. The ADC approach and REACT model can clarify a wide set of data from empirical moral psychology and could inform future studies on moral judgment, as well as case assessments and discussions about issues causing “deadlocked” moral intuitions. (shrink)

The purpose of this paper is to provide a simple yet comprehensive organizing scheme for the responsible conduct of research (RCR). The heuristic offered here should prove helpful in research ethics education, where the many and heterogeneous elements of RCR can be bewildering, as well as research into research integrity and efforts to form RCR policy and regulations. The six domains are scientific integrity, collegiality, protection of human subjects, animal welfare, institutional integrity, and social responsibility.

The effectiveness of decision rules depends on characteristics of both rules and environments. A theoretical analysis of environments specifies the relative predictive accuracies of the “take-the-best” heuristic (TTB) and other simple strategies for choices between two outcomes based on binary cues. We identify three factors: how cues are weighted; characteristics of choice sets; and error. In the absence of error and for cases involving from three to five binary cues, TTB is effective across many environments. However, hybrids of equal (...) weights (EW) and TTB models are more effective as environments become more compensatory. As error in the environment increases, the predictive ability of all models is systematically degraded. Indeed, using the datasets of Gigerenzer et al. (1999, Simple Heuristics That Make Us Smart, New York: Oxford University Press), TTB and similar models do not predict much better than a naïve model that exploits dominance. Finally, we emphasize that the results reported here are conditional on binary cues. (shrink)

Fast and frugal heuristics function accurately and swiftly over a wide range of decision making processes. The performance of these algorithms in the social domain would be an object for research. The use of simple algorithms to investigate social decision-making could prove fruitful in studies of nonhuman primates as well as humans.

Intractability and optimality are two sides of one coin: Optimal models are often intractable, that is, they tend to be excessively complex, or NP-hard. We explain the meaning of NP-hardness in detail and discuss how modem computer science circumvents intractability by introducing heuristics and shortcuts to optimality, often replacing optimality by means of sufficient sub-optimality. Since the principles of decision theory dictate balancing the cost of computation against gain in accuracy, statistical inference is currently being reshaped by a vigorous (...) new trend: the science of simplicity. Simple models, as we show for specific cases, are not just tractable, they also tend to be robust. Robustness is the ability of a model to extract relevant information from data, disregarding noise.Recently, Gigerenzer, Todd and the ABC Research Group (1999) have put forward a collection of fast and frugal heuristics as simple, boundedly rational inference strategies used by the unaided mind in real world inference problems. This collection of heuristics has suggestively been called the adaptive toolbox. In this paper we will focus on a comparison task in order to illustrate the simplicity and robustness of some of the heuristics in the adaptive toolbox in contrast to the intractability and the fragility of optimal solutions. We will concentrate on three important classes of models for comparison-based inference and, in each of these classes, search for that to be used as benchmarks to evaluate the performance of fast and frugal heuristics: lexicographic trees, linear modes and Bayesian networks. Lexicographic trees are interesting because they are particularly simple models that have been used by humans throughout the centuries. Linear models have been traditionally used by cognitive psychologists as models for human inference, while Bayesian networks have only recently been introduced in statistics and computer science. Yet it is the Bayesian networks that are the best possible benchmarks for evaluating the fast and frugal heuristics, as we will show in this paper. (shrink)

How does a complex organism develop from a relatively simple, homogeneous mass? The usual answer is: through the (context‐dependent) execution of species‐specific genetic instructions specifying the development of that organism. Commentators are sometimes skeptical of this usual answer, but of course not all commentators, and not always for the same reasons. Here I attempt to lay bare the logical structure of the usual answer through an extended analysis of the heuristics and methodological principles at play in the exploration (...) and explanation of development—and also to show a critical ambiguity that renders the usual answer suspect. (shrink)

We study repeated normal form games where the number of players is large. We argue that it is interesting to look at such games as being divided into subgames, each of which we call a neighbourhood. The structure of such a game is given by a graph G whose nodes are players and edges denote visibility. The neighbourhoods are maximal cliques in G. The game proceeds in rounds where in each round the players of every clique X of G play (...) a strategic form game among each other. A player at a node v strategises based on what she can observe, i.e., the strategies and the outcomes in the previous round of the players at vertices adjacent to v. Based on this, the player may switch strategies in the same neighbourhood, or migrate to another neighbourhood.We are interested in addressing questions regarding the eventual stability of such games. We incrementally impose constraints on the ‘types’ of the players. First, we look at players who are unconstrained in their strategising abilities, in that, players who may use unbounded memory. We then consider the case of memoryless players. We show that in both these cases the eventual stability of the game can be characterised in terms of potentials. We then introduce a simple modal logic in which the types of the players can be specified. We show that when the players play according to these specified types, it can be effectively decided whether the game stabilises. Finally, we look at the important heuristic of imitation. Simple imitative strategies can be specified in the logic introduced by us. We show that in a population of optimisers and imitators, we can decide how ‘worse-off’ the imitators are by playing imitative strategies rather than optimal ones. (shrink)