QUALITATIVE ANSWERING SURVEYS AND SOFT COMPUTING

In this work, we reflect on some questions about the measurement problem in economics and, especially, their relationship with the scientific method. Statistical sources frequently used by economists contain qualitative information obtained from verbal expressions of individuals by means of surveys, and we discuss the reasons why it would be more adequately analyzed with soft methods than with traditional ones. Some comments on the most commonly applied techniques in the analysis of these types of data with verbal answers are followed by our proposal to compute with words. In our view, an alternative use of the well known Income Evaluation Question seems especially suggestive for a computing with words approach, since it would facilitate an empirical estimation of the corresponding linguistic variable adjectives. A new treatment of the information contained in such surveys would avoid some questions incorporated in the so called Leyden approach that do not fit to the actual world.Computing with words, Leyden approach, qualitative answering surveys, fuzzy logic

Fund managers - why the best might be the worst: On the evolutionary vigor of risk-seeking behavior

This article explores the influence of competitive conditions on the evolutionary fitness of different risk preferences. As a practical example, the professional competition between fund managers is considered. To explore how different settings of competition parameters, the exclusion rate and the exclusion interval, affect individual investment behavior, an evolutionary model based on a genetic algorithm is developed. The simulation experiments indicate that the influence of competitve conditions on investment behavior and attitudes towards risk is significant. What is alarming is that intense competitive pressure generates riskseeking behavior and undermines the predominance of the most skilled. --risk preferences,competition,genetic programming,fund managers,portfolio theory

The Dynamics of Law Clerk Matching: An Experimental and Computational Investigation of Proposals for Reform of the Market

In September of 1998, the Judicial Conference of the United States abandoned as unsuccessful the attempt—the sixth since 1978—to regulate the dates at which law students are hired as clerks by Federal appellate judges. The market promptly resumed the unraveling of appointment dates that had been temporarily slowed by these efforts. In the academic year 1999-2000 many judges hired clerks in the fall of the second year of law school, almost two years before employment would begin, and before hardly any information about candidates other than first year grades was available. Hiring dates moved still earlier in the Fall of 2000 and 2001. The present paper explores proposed reforms of the market, experimentally in the laboratory, and computationally using genetic algorithms. Our results suggest that some of the special features of the judge/law-clerk market—in particular the feeling among many students and judges that students must accept offers when they are made--present obstacles to the success of the proposed reforms, including the latest reform proposed by the judges, in March 2002, which is a one year moratorium on clerkship hiring. Unlike many markets in which the inability to make binding contracts contributes to market failure, in the law clerk market it is the ease with which binding contracts are forged that harms efficiency.

Stochastic Optimization in Econometric Models – A Comparison of GA, SA and RSG

This paper shows that, in case of an econometric model with a high sensitivity to data, using stochastic optimization algorithms is better than using classical gradient techniques. In addition, we showed that the Repetitive Stochastic Guesstimation (RSG) algorithm –invented by Charemza-is closer to Simulated Annealing (SA) than to Genetic Algorithms (GAs), so we produced hybrids between RSG and SA to study their joint behavior. The evaluation of all algorithms involved was performed on a short form of the Romanian macro model, derived from Dobrescu (1996). The subject of optimization was the model’s solution, as function of the initial values (in the first stage) and of the objective functions (in the second stage). We proved that a priori information help “elitist “ algorithms (like RSG and SA) to obtain best results; on the other hand, when one has equal believe concerning the choice among different objective functions, GA gives a straight answer. Analyzing the average related bias of the model’s solution proved the efficiency of the stochastic optimization methods presented.underground economy, Laffer curve, informal activity, fiscal policy, transitionmacroeconomic model, stochastic optimization, evolutionary algorithms, Repetitive Stochastic Guesstimation

Problematising upstream technology through speculative design: the case of quantified cats and dogs

There is growing interest in technology that quantifies aspects of our lives. This paper draws on critical practice and speculative design to explore, question and problematise the ultimate consequences of such technology using the quantification of companion animals (pets) as a case study. We apply the concept of ‘moving upstream’ to study such technology and use a qualitative research approach in which both pet owners, and animal behavioural experts, were presented with, and asked to discuss, speculative designs for pet quantification applications, the design of which were extrapolated from contemporary trends. Our findings indicate a strong desire among pet owners for technology that has little scientific justification, whilst our experts caution that the use of technology to augment human-animal communication has the potential to disimprove animal welfare, undermine human-animal bonds, and create human-human conflicts. Our discussion informs wider debates regarding quantification technology

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions