Since the last decades have been characterized by a strong disillusionment with the performances of the state, especially in countries of SubSaharan Africa, and since market imperfections are a pervasive feature of the rural economy in developing regions, it is perhaps not surprising that many professional and academic economists have looked increasingly to rural communities as a providential escape out of the state-market dilemma. In this way, they have got nearer to their colleagues from other social sciences for whom personalized relationships sealed by various forms of reciprocal exchanges in rural communities allow villagers to effectively solve important problems and to increase their welfare. New strategies based on the idea of decentralized or participatory development have gained increasing currency among international organizations (including the World Bank and the European Community) and bilateral aid institutions. If the idea is anything but new (it has been implemented for a long time by Non-Governmental Organizations, especially in Asia and Latin

System-theoretic accounts of the epistemological processes underlying knowledge acquisition have been shown to apply to both individual human behavior and social development processes, and to enable algorithms to be developed for computer-based systems modeling. Such accounts are applicable to the upper levels of the hierarchy of autonomous systems to provide models of socio-economic behavior. In this paper they are applied to the development of information technology, and used to account for past events and predict future trends in relevant industries such as computing and genetic engineering. Underlying all developments in information technology is a tiered succession of learning curves which make up the infrastructure of the relevant industries. The paper provides a framework for the industries based on this logical progression of developments. It links this empirically to key events in the development of computing and genetic engineering. It links it theoretically to a model of eco...

Philosophical theories summarized here include regularity and necessity theories from Hume to the present; manipulability theory; the theory of powerful particulars; causation as connected changes within a defined state of affairs; departures from "normal " events or from some standard for compar-ison; causation as a transfer of something between objects; and causal propagation and production. Issues found in this literature and of relevance for psychology include whether actual causal relations can be perceived or known; what sorts of things people believe can be causes; different levels of causal analysis; the distinction between the causal relation itself and cues to causal relations; causal frames or fields; internal and external causes; and understanding of causation in different realms of the world, such as the natural and artificial realms. A full theory of causal inference by laypeople should address all of these issues. The main purpose of this article is to survey philosophical theories of causation in a manner intended to be suitable for psychologists interested in causation. The article has two sec-tions: The first presents brief summaries of philosophical theo-ries of causation from Aristotle to the present. In the second, issues found in the philosophical literature are used to suggest new approaches to the study of causation in psychology. Philosophical Theories of Causation Several psychologists have written about selected philosophi-cal theories of causation (Cook & Campbell, 1979; Einhorn &

Experts may be modeled as managing the inductive dynamics of knowledge acquisition in the knowledge processes of society. Who becomes an expert may be modeled as a random process under the influence of strong positive feedback loops in the social mechanisms giving access to knowledge. These models have implications for the design of expert systems.

Emotions influence our decisions. They do so in just about every walk of our lives, whether we are aware or unaware of it and whether we acknowledge it or not. In particular, I will argue that emotions may explain inconsistencies in human behavior and forms of behavior that some have deemed irrational, though such behavior may seem more sensible after a discussion of the functions that emotions serve—or may have once served in our evolutionary past. People do have the capacity to override emotional responses. This capacity relies in large measure on the most recently evolved parts of our brains that support forms of behavior that are more recognizably rational. Neuroscientists are beginning to make headway in identifying the neural mechanisms involved in both emotional responses and higher cognitive processes. This effort offers the promise of a deeper understanding of how and why emotions impact decision making, how this may contribute to behavior that appears to deviate from optimality, and how and when we are able to overcome such emotional responses. In this article I will review a series of human brain imaging studies of decisionmaking behavior. I will argue that the human brain is best understood as a confederation of mechanisms that usually act together, but at times may come into competition with one another, favoring different evaluations of similar circumstances. Modern brain imaging methods allow us to measure this competition and relate it to behavioral outcomes. I will describe examples of these processes, drawn from different domains of psychological function. These examples provide evidence that competition among different brain mechanisms can provide insight into the seemingly inconsistent or irrational responses that are of such interest to behavioral scientists. In particular, I will entertain the idea that a broad range of

The dispute between compatibilists and incompatibilists must be one of the most persistent and heated deadlocks in Western philosophy. Incompatibilists maintain that people are not fully morally responsible if determinism is true, i.e., if every event is an inevitable consequence of the prior conditions and the natural laws. By contrast,

Abstract: A naturalistic scheme of primitive conceptual representations is proposed using the statistical measure of mutual information. It is argued that a concept represents, not the class of objects that caused its tokening, but the class of objects that is most likely to have caused it (had it been tokened), as specified by the statistical measure of mutual information. This solves the problem of misrepresentation which plagues causal accounts, by taking the representation relation to be determined via ordinal relationships between conditional probabilities. The scheme can deal with statistical biases and does not rely on arbitrary criteria. Implications for the theory of meaning and semantic content are addressed. 1.

Nearly all listeners consider the subjective aspects of music, such as its emotional tone, to have primary importance. But contemporary philosophers often downplay, ignore, or even deny such aspects of experience. Moreover, traditional philosophies of music try to decontextualize it. Using music as an example, this paper explores the structure of qualitative experience, demonstrating that it is multi-layer emergent, non-compositional, enacted, and situation dependent, among other non-Cartesian properties.

If you trained someone to emit a particular sound at the sight of something red, another at the sight of something yellow, and so on for other colors, still he would not yet be describing objects by their colors. Though he might be a help to us in giving a description. A description is a representation of a distribution in a space (in that of time, for instance).

Abstract. When a model does not satisfy a given specification, a counterexample is produced by the model checker to demonstrate the failure. A user must then examine the counterexample trace, in order to visually identify the failure that it demonstrates. If the trace is long, or the specification is complex, finding the failure in the trace becomes a non-trivial task. In this paper, we address the problem of analyzing a counterexample trace and highlighting the failure that it demonstrates. Using the notion of causality, introduced by Halpern and Pearl, we formally define a set of causes for the failure of the specification on the given counterexample trace. These causes are marked as red dots and presented to the user as a visual explanation of the failure. We study the complexity of computing the exact set of causes, and provide a polynomial-time algorithm that approximates it. This algorithm is implemented as a feature in the IBM formal verification platform RuleBase PE, where these visual explanations are an integral part of every counterexample trace. Our approach is independent of the tool that produced the counterexample, and can be applied as a light-weight external layer to any model checking tool, or used to explain simulation traces. 1