Towards the Development of a Simulator for Investigating the Impact of People Management Practices on Retail Performance

Often models for understanding the impact of management practices on retail performance are developed under the assumption of stability, equilibrium and linearity, whereas retail operations are considered in reality to be dynamic, non-linear and complex. Alternatively, discrete event and agent-based modelling are approaches that allow the development of simulation models of heterogeneous non-equilibrium systems for testing out different scenarios. When developing simulation models one has to abstract and simplify from the real world, which means that one has to try and capture the 'essence' of the system required for developing a representation of the mechanisms that drive the progression in the real system. Simulation models can be developed at different levels of abstraction. To know the appropriate level of abstraction for a specific application is often more of an art than a science. We have developed a retail branch simulation model to investigate which level of model accuracy is required for such a model to obtain meaningful results for practitioners.Comment: 24 pages, 7 figures, 6 tables, Journal of Simulation 201

DESIGN OF A STATED RANKING EXPERIMENT TO STUDY INTERACTIVE FREIGHT BEHAVIOUR: AN APPLICATION TO ROME'S LTZ

City logistics policies require an understanding of several issues (e.g. freight distribution context, preferences and relationship among agents) seldom accounted for in current research. Policies run the risk of producing unsatisfactory results because behavioural and contextual aspects are not considered. The acquisition of relevant data is crucial to test hypothesis and forecast agents' reactions to policy changes. Despite recent methodological advances in modelling interactive behaviour the development of apt survey instruments is still lacking to test innovative policies acceptability. This paper expands and innovate the methodological literature by describing a stated ranking experiment to study freight agent interactive behaviour and discusses the experimental design implemented to incorporate agent-specific priors when efficient design techniques are employed.urban freight distribution, group decision making, agent-specific interaction, stated preference, stated ranking experiments

The Return to Schooling in Structural Dynamic Models: A Survey

This papers contains a survey of the recent literature devoted to the returns to schooling within a dynamic structural framework. I present a historical perspective on the evolution of the literature, from early static models set in a selectivity framework (Willis and Rosen, 1979) to the recent literature, stimulated by Keane and Wolpin (1997), and which uses stochastic dynamic programming techniques. After reviewing the literature thoroughly, I compare the structural approach with the IV (experimental) approach. I present their commonalities and I also discuss their fundamental differences. To get an order of magnitude, most structural estimates reported for the US range between 4% and 7% per year. On the other hand, IV estimates between 10% and 15% per year are often reported. The discrepancy prevails even when comparable (if not identical) data sets are used. The discussion is focussed on understanding this divergence. The distinction between static and dynamic model specifications is a recurrent theme in the analysis. I argue that the distinction between the IV approach and the structural approach may be coined in terms of a trade off between behavioral and statistical assumptions. For this reason, and unless one has very specific knowledge of the true data generating process, it is neither possible, nor sensible, to claim which approach to estimation is more flexible. More precisely, I show that structural and IV approaches differ mainly at the level of i) the compatibility of the underlying models with truly dynamic behavior, ii) the role of heterogeneity in ability and tastes, iii) the consideration of post-schooling opportunities, and (iv) the specification (and interpretation) of the Mincer wage equation.Returns to Schooling ; Human Capital ; Ability Bias ; Dynamic Programming ; Dynamic Self-Selection ; Natural experiments ; IV estimation

A Multi-Agent Simulation of Retail Management Practices

We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents do offer potential for developing organizational capabilities in the future. Our multi-disciplinary research team has worked with a UK department store to collect data and capture perceptions about operations from actors within departments. Based on this case study work, we have built a simulator that we present in this paper. We then use the simulator to gather empirical evidence regarding two specific management practices: empowerment and employee development

Hilbert Space Embeddings of POMDPs

A nonparametric approach for policy learning for POMDPs is proposed. The approach represents distributions over the states, observations, and actions as embeddings in feature spaces, which are reproducing kernel Hilbert spaces. Distributions over states given the observations are obtained by applying the kernel Bayes' rule to these distribution embeddings. Policies and value functions are defined on the feature space over states, which leads to a feature space expression for the Bellman equation. Value iteration may then be used to estimate the optimal value function and associated policy. Experimental results confirm that the correct policy is learned using the feature space representation.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012