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

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

04
Ago
12

Chaos and physiology: deterministic chaos in exc… [Physiol Rev. 1994] – PubMed – NCBI

See on Scoop.itBounded Rationality and Beyond

PubMed comprises more than 21 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

See on www.ncbi.nlm.nih.gov

04
Ago
12

Stock Trading Robot Makes Decisions Based on Superstitious Algorithms

See on Scoop.itSocial Foraging

When we feel there’s a situation out of our control, we often fall back on superstition to account for it. (“Nothing else is working, why not blame it on that black cat?”) But when enough of us rely on superstition, it’s not just an individual comfort; it starts to have real repercussions. Now a designer has created an algorithm trades stock superstitiously, and it’s going to see if gambling based on full moons and thirteens can pay off.

 

Sid the Superstitious Robot (for which you can see the open-sourced code if you’re so inclined) is governed by a set of rules programmed by 25-year-old Shing Tat Chung. Among them are a phobia of the number thirteen that prevents it from trading stocks on the thirteenth day of the month. On the other side of the scale, it has an affinity for new moons, but will sell during a full moon. It’s a rewiring of other trading systems that make decisions based on more rational changes, such as costs of certain goods or other expected outcomes.

 

But those beliefs aren’t concretely set; Sid incorporates new ones based on feedback from his performance. That doesn’t equate to rationality: a certain pattern can be observed but also be imaginary, and the algorithm will incorporate it based on a superstitious “feeling” that it evokes.

See on www.popsci.com

04
Ago
12

Human cycles: History as science: Mathematical Model Proves History Does Repeat Itself

See on Scoop.itSocial Foraging

Sometimes, history really does seem to repeat itself. After the US Civil War, for example, a wave of urban violence fuelled by ethnic and class resentment swept across the country, peaking in about 1870. Internal strife spiked again in around 1920, when race riots, workers’ strikes and a surge of anti-Communist feeling led many people to think that revolution was imminent. And in around 1970, unrest crested once more, with violent student demonstrations, political assassinations, riots and terrorism (see ‘Cycles of violence’).

 

To Peter Turchin, who studies population dynamics at the University of Connecticut in Storrs, the appearance of three peaks of political instability at roughly 50-year intervals is not a coincidence. For the past 15 years, Turchin has been taking the mathematical techniques that once allowed him to track predator–prey cycles in forest ecosystems, and applying them to human history. He has analysed historical records on economic activity, demographic trends and outbursts of violence in the United States, and has come to the conclusion that a new wave of internal strife is already on its way1. The peak should occur in about 2020, he says, and will probably be at least as high as the one in around 1970. “I hope it won’t be as bad as 1870,” he adds.

Turchin’s approach — which he calls cliodynamics after Clio, the ancient Greek muse of history — is part of a groundswell of efforts to apply scientific methods to history by identifying and modelling the broad social forces that Turchin and his colleagues say shape all human societies. It is an attempt to show that “history is not ‘just one damn thing after another’”, says Turchin, paraphrasing a saying often attributed to the late British historian Arnold Toynbee.

See on www.nature.com




Time is real? I think not

agosto: 2012
L M M G V S D
 12345
6789101112
13141516171819
20212223242526
2728293031  

Commenti recenti

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

Unisciti ad altri 1.155 follower

Latest Tweets


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