Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Performance Evaluation - Annual Report Year 3

This report describes the work done and results obtained in third year of the CATNETS project. Experiments carried out with the different configurations of the prototype are reported and simulation results are evaluated with the CATNETS metrics framework. The applicability of the Catallactic approach as market model for service and resource allocation in application layer networks is assessed based on the results and experience gained both from the prototype development and simulations. --Grid Computing

Simulating tourists' behaviour using multi-agent modelling

We discuss who should be in charge of providing data relevant to marketing segmentation for the tourism industry. We describe the difficulties of using the most commonly found consumer behavioural models within an information system, and oppose them to a novel approach in marketing segmentation, based on outgoings analysis. We use agent-modelling techniques, based on cellular automaton rules and stochastic processes to implement our model and generate sales data. We then present our algorithm to identify similarly behaved tourists, showing that the commonly used “nationality” variable for segments discrimination is not efficient. We conclude with some test runs results discussion and possible further research tracks

Simulating tourists' behaviour using multi-agent modelling

We discuss who should be in charge of providing data relevant to marketing segmentation for the tourism industry. We describe the difficulties of using the most commonly found consumer behavioural models within an information system, and oppose them to a novel approach in marketing segmentation, based on outgoings analysis. We use agent-modelling techniques, based on cellular automaton rules and stochastic processes to implement our model and generate sales data. We then present our algorithm to identify similarly behaved tourists, showing that the commonly used “nationality” variable for segments discrimination is not efficient. We conclude with some test runs results discussion and possible further research tracks.Simulation; Stochastic processes; Cellular automata; Tourism; Business; Public Policy Issues; Management techniques; Marketing; Market segmentation; Customer behaviour model

An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea