nep-cmp New Economics Papers
on Computational Economics
Issue of 2005‒11‒09
four papers chosen by
Stan Miles
York University

  1. Baum-Eagon inequality in probabilistic labeling problems By Crescenzio Gallo; Giancarlo de Stasio
  2. Artificial Neural Networks in Finance Modelling By Crescenzio GALLO
  3. The Evolution of Trust and Reputation: Results from Simulation Experiments By Andreas Diekmann; Wojtek Przepiorka
  4. Interacting Microsoft Visual Basic Procedures (Macros) and GIS tools in order to access optimal location and maximum use of railways and railway infrastructures By José Manuel Viegas; Helder Cristovão; João Filipe Camisão Caio Vieira; Elisabete A. Silva

  1. By: Crescenzio Gallo (Università di Foggia-Dipartimento di Scienze Economiche, Matematiche e Statistiche); Giancarlo de Stasio (Università di Foggia)
    Abstract: This work illustrates an approach to the study of labeling, aka 'object classification'. This kind of parallel computing problem well suites to AI applications (pattern recognition, edge detection, etc.) Our target consists in simplifying an overly computationally costly algorithm proposed by Faugeras and Berthod; using Baum-Eagon theorem, we obtained a reduced algorithm which produces results comparable with other more complex approaches.
    Keywords: labeling, artificial intelligence, edge detection, probabilistic algorithms, pixel classification
    JEL: C9
    Date: 2005–09–07
    URL: http://d.repec.org/n?u=RePEc:wpa:wuwpex:0509003&r=cmp
  2. By: Crescenzio GALLO (Università di Foggia-Dipartimento di Scienze Economiche, Matematiche e Statistiche)
    Abstract: The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological “processing”. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to “suggest” how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non- linear models to do forecasting), while in multi-population models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent. A particular application we aim to study is the one regarding “customer profiling”, in which (based on personal and direct relationships) the “buying” behaviour of each customer can be defined, making use of behavioural inference models such as the ones offered by Artificial Neural Networks much better than traditional statistical methodologies.
    Keywords: Artificial Neural Network, Financial Modelling, Customer Profiling
    JEL: C9
    Date: 2005–09–07
    URL: http://d.repec.org/n?u=RePEc:wpa:wuwpex:0509002&r=cmp
  3. By: Andreas Diekmann (ETH Zurich, Department of Social Sciences & Humanities); Wojtek Przepiorka (ETH Zurich, Department of Social Sciences & Humanities)
    Abstract: In online interactions in general, but especially in interactions between buyers and sellers on internet-auction platforms, the interacting parties must deal with trust and cooperation problems. Whether a rating system is able to foster trust and cooperation through reputation and without an external enforcer is an open question. We therefore explore through ecological analysis different buyer and seller strategies in terms of their success and their contribution to supporting or impeding trust and cooperation. In our agent-based model, the interaction between a buyer and a seller is defined by a one-shot trust game with a reputation mechanism. In every interaction, a buyer has complete information about a seller's past behavior. We find that cooperation evolves under two conditions even in the absence of an external sanctioning authority. On the one hand, some minimal fraction of buyers must make use of the sellers’ reputation in their buying strategies and, on the other hand, trustworthy sellers must be given opportunities to gain a good reputation through their cooperative behavior. Despite the apparent usefulness of the reputation mechanism, a small number of deceitful sellers are able to hold their ground.
    Keywords: trust game, reputation, agent-based simulation
    JEL: C9
    Date: 2005–08–30
    URL: http://d.repec.org/n?u=RePEc:wpa:wuwpex:0508005&r=cmp
  4. By: José Manuel Viegas; Helder Cristovão; João Filipe Camisão Caio Vieira; Elisabete A. Silva
    Abstract: Some parts of the Portuguese railway infrastructure have been neglected through time: Rural lines have been abandoned, investment in new infrastructure is sometimes delayed, and marketing strategies to keep or attract more users have not been pursued. Simultaneously, problems with urban congestion, pollution and mobility for the young, the elderly, the poor, and the handicapped are putting forward the discussion about new or more sustainable modes of transportation. Common sense of public officials, other lobbying groups, and the locals demand new, trendy train lines. And while some axes may have the potential to justify rail lines, others seem to lack population or funding to be enabled. One major problem in order to evaluate the worthiness of these rail projects has been the fact that very often the studies of travel demand and physical implantation are done separately. Travel demand analysis is done based on the four-step model (trip generation, distribution, modal split, and network assignment) using survey data and the network system, using a relatively wide zoning. The importance of interacting with other, finer, information (i.e. slope, density of population, environmental sensitivity, or other socio-economic and land use information) with the development of the travel analysis demand will enhance the analysis/results and increase the chance of proposing lines that are both optimal in location and will have the maximum use by the citizens. Off the shelf software is still unable to perform this kind of operations. Some perform the analysis using existing networks, and no information on the land is available besides the zoning system, other software propose lines accordingly to land slopes, but no trip information is included. GIS packages have the capacity to include the land information and some have some transportation analysis, but are lacking computation capabilities and algorithms to perform analysis similar to off-the-shelf transportation software. In order to develop this kind of integrated analysis it is important to have a good knowledge of the algorithms and analysis required by transportation and of the tools/opportunities offered by the GIS packages. This paper presents a methodology that integrates the transportation algorithms with the GIS functionalities, using excel macro-language. The result is an interaction of both travel demand analysis and site selection. The characteristics of the place constrain the travel demand analysis, but on its own the travel demand analysis define not only the buffer of the train line, but systematically enhance the shape of the line and the location of the stops each time the results of a phase of the travel demand analysis is outputted. This paper offers guidelines for those developing travel demand analysis including some site selection criteria, and it can be a starting point for those of whom intend to develop further application of in the GIS fields.
    Date: 2004–08
    URL: http://d.repec.org/n?u=RePEc:wiw:wiwrsa:ersa04p602&r=cmp

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