161,306 research outputs found
Homo Sapiens Sapiens Meets Homo Strategicus at the Laboratory
Homo Strategicus populates the vast plains of Game Theory. He knows all logical implications of his knowledge (logical omniscience) and chooses optimal strategies given his knowledge and beliefs (rationality). This paper investigates the extent to which the logical capabilities of Homo Sapiens Sapiens resemble those possessed by Homo Strategicus. Controlling for other-regarding preferences and beliefs about the rationality of others, we show, in the laboratory, that the ability of Homo Sapiens Sapiens to perform complex chains of iterative reasoning is much better than previously thought. Subjects were able to perform about two to three iterations of reasoning on average.iterative reasoning; depth of reasoning; logical omniscience; rationality; experiments; other-regarding preferences
Reasoning about Emotional Agents
In this paper we are concerned with reasoning about agents with emotions. To be more precise: we aim at a logical account of emotional agents. The very topic may already raise some eyebrows. Reasoning / rationality and emotions seem opposites, and reasoning about emotions or a logic of emotional agents seems a contradiction in terms. However, emotions and rationality are known to be more interconnected than one may suspect. There is psychological evidence that having emotions may help one to do reasoning and tasks for which rationality seems to be the only factor [1]. Moreover, work by e.g. Sloman [5] shows that one may think of designing agentbased systems where these agents show some kind of emotions, and, even more importantly, display behaviour dependent on their emotional state. It is exactly in this sense that we aim at looking at emotional agents: artificial systems that are designed in such a manner that emotions play a role. Also in psychology emotions are viewed as a structuring mechanism. Emotions are held to help human beings to choose from a myriad of possible actions in response to what happens in ou
Can Rats Reason?
Since at least the mid-1980s claims have been made for rationality in rats. For example,
that rats are capable of inferential reasoning (Blaisdell, Sawa, Leising, & Waldmann,
2006; Bunsey & Eichenbaum, 1996), or that they can make adaptive decisions about
future behavior (Foote & Crystal, 2007), or that they are capable of knowledge in
propositional-like form (Dickinson, 1985). The stakes are rather high, because these
capacities imply concept possession and on some views (e.g., Rödl, 2007; Savanah,
2012) rationality indicates self-consciousness. I evaluate the case for rat rationality by
analyzing 5 key research paradigms: spatial navigation, metacognition, transitive
inference, causal reasoning, and goal orientation. I conclude that the observed behaviors
need not imply rationality by the subjects. Rather, the behavior can be accounted
for by noncognitive processes such as hard-wired species typical predispositions or
associative learning or (nonconceptual) affordance detection. These mechanisms do not
necessarily require or implicate the capacity for rationality. As such there is as yet
insufficient evidence that rats can reason. I end by proposing the âStaircase Test,â an
experiment designed to provide convincing evidence of rationality in rats
A semantical approach to equilibria and rationality
Game theoretic equilibria are mathematical expressions of rationality.
Rational agents are used to model not only humans and their software
representatives, but also organisms, populations, species and genes,
interacting with each other and with the environment. Rational behaviors are
achieved not only through conscious reasoning, but also through spontaneous
stabilization at equilibrium points.
Formal theories of rationality are usually guided by informal intuitions,
which are acquired by observing some concrete economic, biological, or network
processes. Treating such processes as instances of computation, we reconstruct
and refine some basic notions of equilibrium and rationality from the some
basic structures of computation.
It is, of course, well known that equilibria arise as fixed points; the point
is that semantics of computation of fixed points seems to be providing novel
methods, algebraic and coalgebraic, for reasoning about them.Comment: 18 pages; Proceedings of CALCO 200
The neural basis of bounded rational behavior
Bounded rational behaviour is commonly observed in experimental games and in real life situations. Neuroeconomics can help to understand the mental processing underlying bounded rationality and out-of-equilibrium behaviour. Here we report results from recent studies on the neural basis of limited steps of reasoning in a competitive setting â the beauty contest game. We use functional magnetic resonance imaging (fMRI) to study the neural correlates of human mental processes in strategic games. We apply a cognitive hierarchy model to classify subjectâs choices in the experimental game according to the degree of strategic reasoning so that we can identify the neural substrates of different levels of strategizing. We found a correlation between levels of strategic reasoning and activity in a neural network related to mentalizing, i.e. the ability to think about otherâs thoughts and mental states. Moreover, brain data showed how complex cognitive processes subserve the higher level of reasoning about others. We describe how a cognitive hierarchy model fits both behavioural and brain data.Game theory, Bounded rationality, Neuroeconomics
Quotient spaces of boundedly rational types
By identifying types whose low-order beliefs â up to level li â about the state of nature coincide, we obtain quotient type spaces that are typically smaller than the original ones, preserve basic topological properties, and allow standard equilibrium analysis even under bounded reasoning. Our Bayesian Nash (li; l-i)-equilibria capture playersâ inability to distinguish types belonging to the same equivalence class. The case with uncertainty about the vector of levels (li; l-i) is also analyzed. Two examples illustrate the constructions.Incomplete-information games, high-order reasoning, type space, quotient space, hierarchies of beliefs, bounded rationality
Plausible reasoning for the problems of cognitive sociology
The plausible reasoning class (called the JSM-reasoning in honour of John Stuart Mill) is described. It implements interaction of three forms of non-deductive procedures induction, analogy and abduction. Empirical induction in the JSM-reasoning is the basis for generation of hypotheses on causal relations (determinants of social behaviour). Inference by analogy means that predictions about previously unknown properties of objects (individualâs behaviour) are inferred from causal relations. Abductive inference is performed to check on the explanatory adequacy of generated hypotheses. To recognize rationality of respondentsâ opinion deductive inference is used. Plausible reasoning, semantics of argumentation logic and deductive recognition of opinion rationality represent logical tool for cognitive sociology problems
Measuring Higher-Order Rationality with Belief Control
Determining an individual's strategic reasoning capability based solely on
choice data is a complex task. This complexity arises because sophisticated
players might have non-equilibrium beliefs about others, leading to
non-equilibrium actions. In our study, we pair human participants with computer
players known to be fully rational. This use of robot players allows us to
disentangle limited reasoning capacity from belief formation and social biases.
Our results show that, when paired with robots, subjects consistently
demonstrate higher levels of rationality and maintain stable rationality levels
across different games compared to when paired with humans. This suggests that
strategic reasoning might indeed be a consistent trait in individuals.
Furthermore, the identified rationality limits could serve as a measure for
evaluating an individual's strategic capacity when their beliefs about others
are adequately controlled.Comment: The experimental design and the analysis plan are pre-registered on
Open Science Framework (https://osf.io/gye4u/). The experimental instructions
can be found at https://mjfong.github.io/SI_MHOR_final.pd
What Can Information Encapsulation Tell Us About Emotional Rationality?
What can features of cognitive architecture, e.g. the information encapsulation of certain emotion processing systems, tell us about emotional rationality? de Sousa proposes the following hypothesis: âthe role of emotions is to supply the insufficiency of reason by imitating the encapsulation of perceptual modesâ (de Sousa 1987: 195). Very roughly, emotion processing can sometimes occur in a way that is insensitive to what an agent already knows, and such processing can assist reasoning by restricting the response-options she considers. This paper aims to provide an exposition and assessment of de Sousaâs hypothesis. I argue information encapsulation is not essential to emotion-driven reasoning, as emotions can determine the relevance of response-options even without being encapsulated. However, I argue encapsulation can still play a role in assisting reasoning by restricting response-options more efficiently, and in a way that ensures which options emotions deem relevant are not overridden by what the agent knows. I end by briefly explaining why this very feature also helps explain how emotions can, on occasion, hinder reasoning
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