Modelling the effects of social networks on activity and travel behaviour

Activity-based models of transport demand are increasingly used by governments, engineering firms and consultants to predict the impact of various design and planning decisions on travel and consequently on noise emissions, energy consumption, accessibility and other performance indicators. In this context, non-discretionary activities, such as work and school, can be relatively easily explained by the traveller’s sociodemographic characteristics and generalised travel costs. However, participation in, and scheduling of, discretionary and joint activities are not so easily redicted. Understanding the social network that lies on top of the spatial network could lead to better prediction of social activity schedules and better forecasts of travel patterns for joint activities. Existing models of activity-travel behaviour do not consider joint activities in detail, except within households to a limited extent. A recent attempt developed at ETH Zurich to incorporate social networks in a single-day optimisation scheduling model did not model joint activities as such, rather rewarding individuals for scheduling activities at the same location and at the same time as their friends. Realistic social networks were also not incorporated. The aim of this thesis is to contribute to this rapidly expanding field by developing a simulation of activity and travel behaviour incorporating social processes and joint activities to investigate the effects on activity and travel behaviour over a simulated period of weeks. The model developed is intended as a proof-of-concept. In order to achieve this aim, an agent-based simulation was designed, implemented in Java, and calibrated and partly verified with real-world data. The model generates activities on a daily basis, including the time of day and duration of the activity. An interaction protocol has been developed to model the activity decision process. Data collected in Eindhoven on social and joint activities and social networks has been used for calibration and verification. Alongside the model development, several issues are addressed, such as exploring which parameters are useful and their effects, the data required for the validation of agent-based travel behaviour models, and whether the addition of social networks to models of this type makes adifference. Sensitivity testing was undertaken to explore the effects of parameters, which was applied to increasingly more complex versions of the model (starting from one day of outputs with no interactions between individuals and finishing with full interactions over many days). This showed that the model performed as expected when certain parameters were altered. Due to the components included in the model, scenarios of interest to policy makers (such as changes in population, land-use changes, and changes in institutional contexts) can be explored. Altering the structure of the in- put social networks and the interaction protocols showed that these inputs do have a difference on the outputs of the model. As a result, these elements of the model require data collection on the social network structure and the decision processes for each local instantiation. Two more "traditional" transport planning policy scenarios, an increase in free time and an increase in travel cost, showed that the model performs as expected for these scenarios. It is shown that the use of agent-based modelling is useful in permitting the incorporation of social networks. The social network can have a significant impact on model results and therefore the decisions made by planners and stakeholders. The model can be extended further in several different directions as new theories are developed and data sets are collected

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Iterated Prisoner's Dilemma with Choice and Refusal of Partners

This article extends the traditional iterated prisoner's dilemma (IPD) with round-robin partner matching by permitting players to choose and refuse partners in each iteration on the basis of continually updated expected payoffs. Comparative computer experiments are reported that indicate the introduction of partner choice and refusal accelerates the emergence of mutual cooperation in the IPD relative to round-robin partner matching. Moreover, in contrast to findings for round-robin partner matching (in which the average payoffs of the players tend to be either clustered around the mutual cooperation payoff or widely scattered), the average payoff scores of the players with choice and refusal of partners tend to cluster into two or more distinct narrow bands. Preliminary analytical and computational sensitivity studies are also reported for several key parameters. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/tnghome.htmiterated prisoner's dilemma; preferential partner selection; evolutionary game theory

Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

An agent-based dynamic information network for supply chain management

One of the main research issues in supply chain management is to improve the global efficiency of supply chains. However, the improvement efforts often fail because supply chains are complex, are subject to frequent changes, and collaboration and information sharing in the supply chains are often infeasible. This paper presents a practical collaboration framework for supply chain management wherein multi-agent systems form dynamic information networks and coordinate their production and order planning according to synchronized estimation of market demands. In the framework, agents employ an iterative relaxation contract net protocol to find the most desirable suppliers by using data envelopment analysis. Furthermore, the chain of buyers and suppliers, from the end markets to raw material suppliers, form dynamic information networks for synchronized planning. This paper presents an agent-based dynamic information network for supply chain management and discusses the associated pros and cons