Cognitivism and Innovation in Economics - Two Lectures

This issue of the Department W.P. reproduces two lectures by Professor Loasby organized by the CISEPS (Centre for Interdisciplinary Studies in Economics, Psychology and the Social Sciences at Bicocca) in collaboration with the IEP, the Istituto di Economia Politica of the Bocconi University in Milan. The first lecture was delivered at the University of Milano-Bicocca on 13 October 2003 and the second was staged the day after at the Bocconi University. The lectures are reproduced here together with a comment by dr. Stefano Brusoni of Bocconi and SPRU. Two further comments were presented at the time by Professor Richard Arena of the University of Nice and by Professor Pier Luigi Sacco of the University of Venice. Both of them deserve gratitude for active participation to the initiative. Unfortunately it has not been possible to include their comments in the printed form. In these lectures Brian Loasby opens under the title of Psychology of Wealth (a title echoing a famous essay by Carlo Cattaneo) and he develops an argument in cognitive economics which is based on Hayek’s theory of the human mind with significant complements and extensions, mainly from Smith and Marshall. The second lecture provides a discussion on organization and the human mind. It can be read independently although it is linked to the former. Indeed, in Professor Loasby’s words, “the psychology of wealth leads to a particular perspective on this problem of organization”. The gist of the argument lies in the need to appreciate the significance of an appropriate “balance between apparently conflicting principles: the coherence, and therefore the effectiveness, of this differentiated system requires some degree of compatibility between its elements, but the creation of differentiated knowledge and skills depends on the freedom to make idiosyncratic patterns by thinking and acting in ways which may be radically different from those of many other people”. This dilemma of compatibility vs. independence can find solution in a variety of contexts, as Loasby’s analysis shows. In his comments Richard Arena had focussed on the rationality issues, so prominent in Loasby’s text. For example, he had suggested that the cleavage between rational choice equilibrium and evolutionary order offers ground to new forms of self-organization. Pier Luigi Sacco had emphasized that Loasby’s approach breaks new ground on the economics of culture and paves the way to less simplistic conceptions of endogenous growth than is suggested by the conventional wisdom of current models. Unfortunately, as hinted above, is has proved impossible to include those comments in the present booklet along with Loasby’s lectures. A special obligation must be recorded to Dr. Stefano Brusoni, who has prepared a written version of his own comment which has been printed in this booklet and can be offered to the reader. Among other participants Roberto Scazzieri, of the University of Bologna, Tiziano Raffaelli, of the University of Pisa, Luigino Bruni of Bicocca, Riccardo Cappellin of Rome ‘Tor Vergata’ and others were able to offer significant comments during the two sessions of the initiative. The organizers are particularly grateful to Professor Brian Loasby for the active and generous support of the initiative. Together with our colleagues and students we have been able to admire his enthusiasm and intellectual creativity in treating some of the more fascinating topics of contemporary economics.

A New Constructivist AI: From Manual Methods to Self-Constructive Systems

The development of artificial intelligence (AI) systems has to date been largely one of manual labor. This constructionist approach to AI has resulted in systems with limited-domain application and severe performance brittleness. No AI architecture to date incorporates, in a single system, the many features that make natural intelligence general-purpose, including system-wide attention, analogy-making, system-wide learning, and various other complex transversal functions. Going beyond current AI systems will require significantly more complex system architecture than attempted to date. The heavy reliance on direct human specification and intervention in constructionist AI brings severe theoretical and practical limitations to any system built that way. One way to address the challenge of artificial general intelligence (AGI) is replacing a top-down architectural design approach with methods that allow the system to manage its own growth. This calls for a fundamental shift from hand-crafting to self-organizing architectures and self-generated code – what we call a constructivist AI approach, in reference to the self-constructive principles on which it must be based. Methodologies employed for constructivist AI will be very different from today’s software development methods; instead of relying on direct design of mental functions and their implementation in a cog- nitive architecture, they must address the principles – the “seeds” – from which a cognitive architecture can automatically grow. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift

A framework for teaching biology using StarLogo TNG : from DNA to evolution

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 65-66).This thesis outlines a 10-unit biology curriculum implemented in StarLogo TNG. The curriculum moves through units on ecology, the DNA-protein relationship, and evolution. By combining the three topics, it aims to highlight the similarities among different scales and the relationships between them. In particular, through the curriculum, students can see how small-scale changes in molecular processes can create large-scale changes in entire populations. In addition, the curriculum encourages students to engage in problembased learning, by which they are trained to approach questions creatively and independently.by Yaa-Lirng Tu.M.Eng

Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Multi-Agents Systems and Territory: Concepts, Methods and Applications

This paper analyses the multi-agents systems that are now considered the best tool to simulate and study real world. We review the main characteristics of a multi-agents system, namely interactions and cooperations of agents, communications and behaviours between them and finally the schedule of actions and jobs assignment to agents. The multi-agents system approach is increasingly applied in social and economic sciences; so we study mainly the territorial applications. In these applications new characteristics arise from the consideration of territory (land and space where the agents live or territory as an agent in itself, that evolves in the time). We study possible new applications of multi-agents applied to the territory (for instance, to define town planning policies or to locate dangerous facilities). Furthermore we study new tools to make operational multi-agents systems (mainly Swarm, the toolkit of Santa Fe Institute). With Swarm we present two kind of territorial applications: with located agents (fixed in space) and with not located agents (moving in the space). Finally we show the results of these applications.

The Nexus between Artificial Intelligence and Economics

This book is organized as follows. Section 2 introduces the notion of the Singularity, a stage in development in which technological progress and economic growth increase at a near-infinite rate. Section 3 describes what artificial intelligence is and how it has been applied. Section 4 considers artificial happiness and the likelihood that artificial intelligence might increase human happiness. Section 5 discusses some prominent related concepts and issues. Section 6 describes the use of artificial agents in economic modeling, and section 7 considers some ways in which economic analysis can offer some hints about what the advent of artificial intelligence might bring. Chapter 8 presents some thoughts about the current state of AI and its future prospects.