Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

The lottery-panel task for bi-dimensional parameter-free elicitation of risk attitudes

We propose first, a simple task for the eliciting attitudes toward risky choice, the SGG lottery-panel task, which consists in a series of lotteries constructed to compensate riskier options with higher risk-return trade-offs. Using Principal Component Analysis technique, we show that the SGG lottery-panel task is capable of capturing two dimensions of individual risky decision making i.e. subjects’ average risk taking and their sensitivity towards variations in risk-return. From the results of a large experimental dataset, we confirm that the task systematically captures a number of regularities such as: A tendency to risk averse behavior (only around 10% of choices are compatible with risk neutrality); An attraction to certain payoffs compared to low risk lotteries, compatible with over-(under-) weighting of small (large) probabilities predicted in PT and; Gender differences, i.e. males being consistently less risk averse than females but both genders being similarly responsive to the increases in risk-premium. Another interesting result is that in hypothetical choices most individuals increase their risk taking responding to the increase in return to risk, as predicted by PT, while across panels with real rewards we see even more changes, but opposite to the expected pattern of riskier choices for higher risk-returns. Therefore, we conclude from our data that an “economic anomaly” emerges in the real reward choices opposite to the hypothetical choices. These findings are in line with Camerer's (1995) view that although in many domains, paid subjects probably do exert extra mental effort which improves their performance, choice over money gambles is not likely to be a domain in which effort will improve adherence to rational axioms (p. 635). Finally, we demonstrate that both dimensions of risk attitudes, average risk taking and sensitivity towards variations in the return to risk, are desirable not only to describe behavior under risk but also to explain behavior in other contexts, as illustrated by an example. In the second study, we propose three additional treatments intended to elicit risk attitudes under high stakes and mixed outcome (gains and losses) lotteries. Using a dataset obtained from a hypothetical implementation of the tasks we show that the new treatments are able to capture both dimensions of risk attitudes. This new dataset allows us to describe several regularities, both at the aggregate and within-subjects level. We find that in every treatment over 70% of choices show some degree of risk aversion and only between 0.6% and 15.3% of individuals are consistently risk neutral within the same treatment. We also confirm the existence of gender differences in the degree of risk taking, that is, in all treatments females prefer safer lotteries compared to males. Regarding our second dimension of risk attitudes we observe, in all treatments, an increase in risk taking in response to risk premium increases. Treatment comparisons reveal other regularities, such as a lower degree of risk taking in large stake treatments compared to low stake treatments and a lower degree of risk taking when losses are incorporated into the large stake lotteries. Results that are compatible with previous findings in the literature, for stake size effects (e.g., Binswanger, 1980; Antoni Bosch-Domènech & Silvestre, 1999; Hogarth & Einhorn, 1990; Holt & Laury, 2002; Kachelmeier & Shehata, 1992; Kühberger et al., 1999; B. J. Weber & Chapman, 2005; Wik et al., 2007) and domain effect (e.g., Brooks and Zank, 2005, Schoemaker, 1990, Wik et al., 2007). Whereas for small stake treatments, we find that the effect of incorporating losses into the outcomes is not so clear. At the aggregate level an increase in risk taking is observed, but also more dispersion in the choices, whilst at the within-subjects level the effect weakens. Finally, regarding responses to risk premium, we find that compared to only gains treatments sensitivity is lower in the mixed lotteries treatments (SL and LL). In general sensitivity to risk-return is more affected by the domain than the stake size. After having described the properties of risk attitudes as captured by the SGG risk elicitation task and its three new versions, it is important to recall that the danger of using unidimensional descriptions of risk attitudes goes beyond the incompatibility with modern economic theories like PT, CPT etc., all of which call for tests with multiple degrees of freedom. Being faithful to this recommendation, the contribution of this essay is an empirically and endogenously determined bi-dimensional specification of risk attitudes, useful to describe behavior under uncertainty and to explain behavior in other contexts. Hopefully, this will contribute to create large datasets containing a multidimensional description of individual risk attitudes, while at the same time allowing for a robust context, compatible with present and even future more complex descriptions of human attitudes towards risk.

The cyclical relations between traded property stock prices and aggregate time-series

This paper examines the cyclical regularities of macroeconomic, financial and property market aggregates in relation to the property stock price cycle in the UK. The Hodrick Prescott filter is employed to fit a long-term trend to the raw data, and to derive the short-term cycles of each series. It is found that the cycles of consumer expenditure, total consumption per capita, the dividend yield and the long-term bond yield are moderately correlated, and mainly coincident, with the property price cycle. There is also evidence that the nominal and real Treasury Bill rates and the interest rate spread lead this cycle by one or two quarters, and therefore that these series can be considered leading indicators of property stock prices. This study recommends that macroeconomic and financial variables can provide useful information to explain and potentially to forecast movements of property-backed stock returns in the UK.

The persistence of distance: a gravity model of European cross-border real estate development flows

Purpose – This paper aims to investigate the scale and drivers of cross-border real estate development in Western Europe and Central and Eastern Europe. Design/methodology/approach – Placing cross-border real estate development within the framework of foreign direct investment (FDI), conceptual complexities in characterizing the notional real estate developer are emphasized. Drawing upon a transaction database, this paper proxies cross-border real estate development flows with asset sales by developers. Findings – Much higher levels of market penetration by international real estate developers are found in the less mature markets of Central and Eastern Europe. Analysis suggests a complex range of determinants with physical distance remaining a consistent barrier to cross-border development flows. Originality/value – This analysis adds significant value in terms of understanding cross-border real estate development flows. In this study, a detailed examination of the issues based on a rigorous empirical analysis through gravity modelling is offered. The gravity framework is one of the most confirmed empirical regularities in international economics and commonly applied to trade, FDI, migration, foreign portfolio investment inter alia. This paper assesses the extent to which it provides useful insights into the pattern of cross-border real estate development flows.

Size matters: body size determines functional responses of ground beetle interactions

Understanding patterns in predator:prey systems and the mechanisms that underlie trophic interactions provides a basis for predicting community structure and the delivery of natural pest control services. The functional response of predators to prey density is a fundamental measure of interaction strength and its characterisation is essential to understanding these processes. We used mesocosm experiments to quantify the functional responses of five ground beetle species that represent common generalist predators of north-west European arable agriculture. We investigated two mechanisms predicted to be key drivers of trophic interactions in natural communities: predator:prey body size ratio and multiple predator effects. Our results show regularities in foraging patterns characteristic of similarly sized predators. Ground beetle attack rates increased and handling times decreased as the predator:prey body-mass ratio rose. Multiple predator effects on total prey consumption rates were sensitive to the identity of the interacting species but not prey density. The extent of interspecific interactions may be a result of differences in body mass between competing beetle species. Overall these results add to the growing evidence for the importance of size in determining trophic interactions and suggest that body mass could offer a focus on which to base the management of natural enemy assemblages.

An incremental FP-growth web content mining and its application in preference identification

This paper presents a real application of Web-content mining using an incremental FP-Growth approach. We firstly restructure the semi-structured data retrieved from the web pages of Chinese car market to fit into the local database, and then employ an incremental algorithm to discover the association rules for the identification of car preference. To find more general regularities, a method of attribute-oriented induction is also utilized to find customer’s consumption preferences. Experimental results show some interesting consumption preference patterns that may be beneficial for the government in making policy to encourage and guide car consumption.

An web content based data mining for car consumption in China

This paper introduces an incremental FP-Growth approach for Web content based data mining and its application in solving a real world problem The problem is solved in the following ways. Firstly, we obtain the semi-structured data from the Web pages of Chinese car market and structure them and save them in local database. Secondly, we use an incremental FP-Growth algorithm for mining association rules to discover Chinese consumers' car consumption preference. To find more general regularities, an attribute-oriented induction method is also utilized to find customer's consumption preference among a range of car categories. Experimental results have revealed some interesting consumption preferences that are useful for the decision makers to make the policy to encourage and guide car consumption. Although the current data we used may not be the best representative of the actual market in practice, it is still good enough for the decision making purpose in terms of reflecting the real situation of car consumption preference under the two assumptions in the context.

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.

Entropy-based collaborative detection of DDOS attacks on community networks

A community network often operates with the same Internet service provider domain or the virtual network of different entities who are cooperating with each other. In such a federated network environment, routers can work closely to raise early warning of DDoS attacks to void catastrophic damages. However, the attackers simulate the normal network behaviors, e.g. pumping the attack packages as poisson distribution, to disable detection algorithms. It is an open question: how to discriminate DDoS attacks from surge legitimate accessing. We noticed that the attackers use the same mathematical functions to control the speed of attack package pumping to the victim. Based on this observation, the different attack flows of a DDoS attack share the same regularities, which is different from the real surging accessing in a short time period. We apply information theory parameter, entropy rate, to discriminate the DDoS attack from the surge legitimate accessing. We proved the effectiveness of our method in theory, and the simulations are the work in the near future. We also point out the future directions that worth to explore in the future.

Constructing the monster : notions of the monstrous in classical antiquity

Focussing on humaniod monsters, this thesis uses insights from Foucault's theory about the "archaeology" of discourses and Derrida's practice of deconstruction to examine how monstrosity was spoken of in antiquity, and how the various "sciences" dealt with anomalous monsters without jeopardising their epistemological credibility. Discussion begins with a survey of the semantic field of teras and monstrum. Since portentousness was central to both terms, the signification of monstrous portents in divinatory practice is next aalysed in the historiography of Herodotus, Livy, and others. Cicero's De divinatione reveals the theory and the problem for that science posed by accidental monstrosities. Chance and novelty are also issues in mythical and scientific cosmogonies < of Hesiod, and Orphism, Empedo-cles, and Lucretius> , where monsters arise and are dealt with while cosmic regularities, reproductive and ethical, are being established. Teleology and the stability of species'forms emerge as important concerns. These issues are further considered in Aristotle's bioogy and in medical writings from Hippocrates to Galen. There, theories are produced about monstrous embryology which attempt to answer the question of how deformities occur if species' forms are perpetuated through repro-duction. Biological and taxonomic--as well as ethical--boundaries are violated also by mythic human-beast hybrids. Narratives about such anomalies clarify the nature of monstrous deviance and enact solutions to the problem. Their strategies have much in common with other modes of discourse. Ethnography is posed similar questions about monstrous races' physical and ethical deviations from the civilised norm; it speaks of those issues in terms of invariance of form through generations, geographical remoteness and the codes which situate those races ethically. Finally, Augustine’s discourse on monstrous individuals and races is examined as Christianity’s belief in God’s governance reformulates the ancient’s discussions of chane or novelty and the invariance of species. In all these discourses founded on determinate meaning, the persistant paradox of monstrosity need offer no challenge to rationality provided its indefinable diversity is unacknowledged and the notion is constructed in such a way as to reaffirm the certainties.

Portfolio diversification and share price behavior : some evidence from the Taiwan Stock Exchange

This dissertation consists of four separate but closely related studies which investigate different aspects of share price behavior on the Taiwan Stock Exchange over the period 1980-89: 1.The benefits of diversification available to investors using the Markowitz model and the Single Model Index. 2. The applicability of the CAPM to the TSE over the decade. 3. Regularities in proce sequences. 4. Market reaction to the announcements of stock dividends, right issues and combinations of both.

Learning Boltzmann distance metric for face recognition

We introduce a new method for face recognition using a versatile probabilistic model known as Restricted Boltzmann Machine (RBM). In particular, we propose to regularise the standard data likelihood learning with an information-theoretic distance metric defined on intra-personal images. This results in an effective face representation which captures the regularities in the face space and minimises the intra-personal variations. In addition, our method allows easy incorporation of multiple feature sets with controllable level of sparsity. Our experiments on a high variation dataset show that the proposed method is competitive against other metric learning rivals. We also investigated the RBM method under a variety of settings, including fusing facial parts and utilising localised feature detectors under varying resolutions. In particular, the accuracy is boosted from 71.8% with the standard whole-face pixels to 99.2% with combination of facial parts, localised feature extractors and appropriate resolutions.

Learning sparse latent representation and distance metric for image retrieval

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG.

Área de influência e localização varejista

O trabalho procura dar uma contribuição ao conhecimento varejista no Brasil, integrando aspectos teóricos e empíricos sobre a Área de Influência, conceito de fundamental importância no marketing varejista. A metodologia engloba uma revisão e reordenação do conhecimento teórico existente, e desenvolve uma investigação sobre o fenômeno da Área de Influência com base em pesquisa empírica com clientes de supermercados brasileiros. Através desta análise, chegou-se a conclusão de que as Áreas de Influência de diferentes supermercados, apesar de terem dimensões e comportamentos muito variados, guardam um padrão de distribuição geográfica com características semelhantes. A descoberta de certas generalizações ocorreu quando analisamos o fenômeno da área de influência através de curvas acumuladas de clientes. Verificamos também que o tamanho da loja e a densidade populacional da região onde está localizada parecem ser fatores determinantes da extensão da área de influência.

Essays on regulation and risk

In this thesis, we investigate some aspects of the interplay between economic regulation and the risk of the regulated firm. In the first chapter, the main goal is to understand the implications a mainstream regulatory model (Laffont and Tirole, 1993) have on the systematic risk of the firm. We generalize the model in order to incorporate aggregate risk, and find that the optimal regulatory contract must be severely constrained in order to reproduce real-world systematic risk levels. We also consider the optimal profit-sharing mechanism, with an endogenous sharing rate, to explore the relationship between contract power and beta. We find results compatible with the available evidence that high-powered regimes impose more risk to the firm. In the second chapter, a joint work with Daniel Lima from the University of California, San Diego (UCSD), we start from the observation that regulated firms are subject to some regulatory practices that potentially affect the symmetry of the distribution of their future profits. If these practices are anticipated by investors in the stock market, the pattern of asymmetry in the empirical distribution of stock returns may differ among regulated and non-regulated companies. We review some recently proposed asymmetry measures that are robust to the empirical regularities of return data and use them to investigate whether there are meaningful differences in the distribution of asymmetry between these two groups of companies. In the third and last chapter, three different approaches to the capital asset pricing model of Kraus and Litzenberger (1976) are tested with recent Brazilian data and estimated using the generalized method of moments (GMM) as a unifying procedure. We find that ex-post stock returns generally exhibit statistically significant coskewness with the market portfolio, and hence are sensitive to squared market returns. However, while the theoretical ground for the preference for skewness is well established and fairly intuitive, we did not find supporting evidence that investors require a premium for supporting this risk factor in Brazil.