Archivio per 4 agosto 2012

04
Ago
12

Bounded Rationality – Grüne-Yanoff – 2007 – Philosophy Compass – Wiley Online Library

See on Scoop.itBounded Rationality and Beyond

Abstract

The notion of bounded rationality has recently gained considerable popularity in the behavioural and social sciences. This article surveys the different usages of the term, in particular the way ‘anomalosus’ behavioural phenomena are elicited, how these phenomena are incorporated in model building, and what sort of new theories of behaviour have been developed to account for bounded rationality in choice and in deliberation. It also discusses the normative relevance of bounded rationality, in particular as a justifier of non-standard reasoning and deliberation heuristics. For each of these usages, the overview discusses the central methodological problems.

See on onlinelibrary.wiley.com

Annunci
04
Ago
12

Towards Competitive Instead of Biased Testing of Heuristics: A Reply to Hilbig and Richter (2011) – Brighton – 2011 – Topics in Cognitive Science – Wiley Online Library

See on Scoop.itBounded Rationality and Beyond

Abstract

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.

See on onlinelibrary.wiley.com

04
Ago
12

Homo heuristicus Outnumbered: Comment on Gigerenzer and Brighton (2009) – Hilbig – 2011 – Topics in Cognitive Science – Wiley Online Library

See on Scoop.itBounded Rationality and Beyond

Abstract

Gigerenzer and Brighton (2009) have argued for a “Homo heuristicus” view of judgment and decision making, claiming that there is evidence for a majority of individuals using fast and frugal heuristics. In this vein, they criticize previous studies that tested the descriptive adequacy of some of these heuristics. In addition, they provide a reanalysis of experimental data on the recognition heuristic that allegedly supports Gigerenzer and Brighton’s view of pervasive reliance on heuristics. However, their arguments and reanalyses are both conceptually and methodologically problematic. We provide counterarguments and a reanalysis of the data considered by Gigerenzer and Brighton. Results clearly replicate previous findings, which are at odds with the claim that simple heuristics provide a general description of inferences for a majority of decision makers.

See on onlinelibrary.wiley.com

04
Ago
12

How Does the Mind Work? Insights from Biology – Marcus – 2009 – Topics in Cognitive Science – Wiley Online Library

See on Scoop.itBounded Rationality and Beyond

Abstract

Cognitive scientists must understand not just what the mind does, but how it does what it does. In this paper, I consider four aspects of cognitive architecture: how the mind develops, the extent to which it is or is not modular, the extent to which it is or is not optimal, and the extent to which it should or should not be considered a symbol-manipulating device (as opposed to, say, an eliminative connectionist network). In each case, I argue that insights from developmental and evolutionary biology can lead to substantive and important compromises in historically vexed debates.

See on onlinelibrary.wiley.com

04
Ago
12

Moral Satisficing: Rethinking Moral Behavior as Bounded Rationality – Gigerenzer – 2010 – Topics in Cognitive Science – Wiley Online Library

See on Scoop.itBounded Rationality and Beyond

Abstract

What is the nature of moral behavior? According to the study of bounded rationality, it results not from character traits or rational deliberation alone, but from the interplay between mind and environment. In this view, moral behavior is based on pragmatic social heuristics rather than moral rules or maximization principles. These social heuristics are not good or bad per se, but solely in relation to the environments in which they are used. This has methodological implications for the study of morality: Behavior needs to be studied in social groups as well as in isolation, in natural environments as well as in labs. It also has implications for moral policy: Only by accepting the fact that behavior is a function of both mind and environmental structures can realistic prescriptive means of achieving moral goals be developed.

See on onlinelibrary.wiley.com

04
Ago
12

Homo Heuristicus: Why Biased Minds Make Better Inferences – Gigerenzer – 2009 – Topics in Cognitive Science – Wiley Online Library

See on Scoop.itBounded Rationality and Beyond

Abstract

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an “adaptive toolbox;” and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategie

See on onlinelibrary.wiley.com

04
Ago
12

Is there chaos in the brain? II. Experimental evide… [C R Biol. 2003] – PubMed – NCBI

See on Scoop.itBounded Rationality and Beyond

The search for chaotic patterns has occupied numerous investigators in neuroscience, as in many other fields of science. Their results and main conclusions are reviewed in the light of the most recent criteria that need to be satisfied since the first descriptions of the surrogate strategy. The methods used in each of these studies have almost invariably combined the analysis of experimental data with simulations using formal models, often based on modified Huxley and Hodgkin equations and/or of the Hindmarsh and Rose models of bursting neurons. Due to technical limitations, the results of these simulations have prevailed over experimental ones in studies on the nonlinear properties of large cortical networks and higher brain functions. Yet, and although a convincing proof of chaos (as defined mathematically) has only been obtained at the level of axons, of single and coupled cells, convergent results can be interpreted as compatible with the notion that signals in the brain are distributed according to chaotic patterns at all levels of its various forms of hierarchy. This chronological account of the main landmarks of nonlinear neurosciences follows an earlier publication [Faure, Korn, C. R. Acad. Sci. Paris, Ser. III 324 (2001) 773-793] that was focused on the basic concepts of nonlinear dynamics and methods of investigations which allow chaotic processes to be distinguished from stochastic ones and on the rationale for envisioning their control using external perturbations. Here we present the data and main arguments that support the existence of chaos at all levels from the simplest to the most complex forms of organization of the nervous system. We first provide a short mathematical description of the models of excitable cells and of the different modes of firing of bursting neurons (Section 1). The deterministic behavior reported in giant axons (principally squid), in pacemaker cells, in isolated or in paired neurons of Invertebrates acting as coupled oscillators is then described (Section 2). We also consider chaotic processes exhibited by coupled Vertebrate neurons and of several components of Central Pattern Generators (Section 3). It is then shown that as indicated by studies of synaptic noise, deterministic patterns of firing in presynaptic interneurons are reliably transmitted, to their postsynaptic targets, via probabilistic synapses (Section 4). This raises the more general issue of chaos as a possible neuronal code and of the emerging concept of stochastic resonance Considerations on cortical dynamics and of EEGs are divided in two parts. The first concerns the early attempts by several pioneer authors to demonstrate chaos in experimental material such as the olfactory system or in human recordings during various forms of epilepsies, and the belief in ‘dynamical diseases’ (Section 5). The second part explores the more recent period during which surrogate-testing, definition of unstable periodic orbits and period-doubling bifurcations have been used to establish more firmly the nonlinear features of retinal and cortical activities and to define predictors of epileptic seizures (Section 6). Finally studies of multidimensional systems have founded radical hypothesis on the role of neuronal attractors in information processing, perception and memory and two elaborate models of the internal states of the brain (i.e. ‘winnerless competition’ and ‘chaotic itinerancy’). Their modifications during cognitive functions are given special attention due to their functional and adaptive capabilities (Section 7) and despite the difficulties that still exist in the practical use of topological profiles in a state space to identify the physical underlying correlates. The reality of ‘neurochaos’ and its relations with information theory are discussed in the conclusion (Section 8) where are also emphasized the similarities between the theory of chaos and that of dynamical systems. Both theories strongly challenge computationalism and suggest that new models are needed to describe how the external world is represe

See on www.ncbi.nlm.nih.gov




Time is real? I think not

agosto: 2012
L M M G V S D
« Lug   Set »
 12345
6789101112
13141516171819
20212223242526
2728293031  

Commenti recenti

Lorenzo Bosio su Un testo che trascende le sue…

Inserisci il tuo indirizzo e-mail per iscriverti a questo blog e ricevere notifiche di nuovi messaggi per e-mail.

Segui assieme ad altri 1.145 follower

Latest Tweets

Annunci

%d blogger hanno fatto clic su Mi Piace per questo: