An interview with Thomas C. Schelling: Interpretation of game theory and the checkerboard model

This note is mainly based on a short interview with Thomas C. Schelling (TCS), who shared the Nobel Prize with Robert J. Aumann in 2005. The interview took place on 06.03.2001 at University of Maryland, College Park, USA. It consists of two parts. The first part is about his interpretation of game theory, particularly about the use of game-theoretic models in explaining the origin and maintenance of conventions, and norms. The second part is on the origin of Schelling's influential checkerboard model of residential segregation, particularly about his approach to modeling social phenomena exemplified by this model. The note ends with some concluding remarks.checkerboard model

An interview with Thomas C. Schelling: Interpretation of game theory and the checkerboard model

This note is mainly based on a short interview with Thomas C. Schelling (TCS), who shared the Nobel Prize with Robert J. Aumann in 2005. The interview took place on 06.03.2001 at University of Maryland, College Park, USA. It consists of two parts. The first part is about his interpretation of game theory, particularly about the use of game- theoretic models in explaining the origin and maintenance of conventions, and norms. The second part is on the origin of Schelling’s influential checkerboard model of residential segregation, particularly about his approach to modeling social phenomena exemplified by this model. The note ends with some concluding remarks. Citation: Aydinonat, N. Emrah, (2005) 'An interview with Thomas C. Schelling: Interpretation of game theory and the checkerboard model,' Economics Bulletin, Vol. 2 no. 2 pp. 1-7.Thomas Schelling, game theory, checkerboard model

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

Decentralized and adaptive sensor data routing

Wireless sensor network (WSN) has been attracting research efforts due to the rapidly increasing applications in military and civilian fields. An important issue in wireless sensor network is how to send information in an efficient and adaptive way. Information can be directly sent back to the base station or through a sequence of intermediate nodes. In the later case, it becomes the problem of routing. Current routing protocols can be categorized into two groups, namely table-drive (proactive) routing protocols and source-initiated on-demand (reactive) routing. For ad hoc wireless sensor network, routing protocols must deal with some unique constraints such as energy conservation, low bandwidth, high error rate and unpredictable topology, of which wired network might not possess. Thus, a routing protocol, which is energy efficient, self-adaptive and error tolerant is highly demanded. A new peer to peer (P2P) routing notion based on the theory of cellular automata has been put forward to solve this problem. We proposed two different models, namely Spin Glass (Physics) inspired model and Multi-fractal (Chemistry) inspired model. Our new routing models are distributed in computation and self-adaptive to topological disturbance. All these merits can not only save significant amount of communication and computation cost but also well adapt to the highly volatile environment of ad hoc WSN. With the cellular automata Cantor modeling tool, we implemented two dynamic link libraries (DLL) in C++ and the corresponding graphic display procedures in Tcl/tk. Results of each model’s routing ability are discussed and hopefully it will lead to new peer to peer algorithms, which can combine the advantages of current models

From Models to Simulations

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations

Projecting land use changes using parcel-level data : model development and application to Hunterdon County, New Jersey

This dissertation is to develop a parcel-based spatial land use change prediction model by coupling various machine learning and interpretation algorithms such as cellular automata (CA) and decision tree (DT). CA is a collection of cells that evolves through a number of discrete time steps according to a set of transition rules based on the state of each cell and the characteristics of its neighboring cells. DT is a data mining and machine learning tool that extracts the patterns of decision process from observed cell behaviors and their affecting factors. In this dissertation, CA is used to predict the future land use status of cadastral parcels based on a set of transition rules derived from a set of identified land use change driving factors using DT. Although CA and DT have been applied separately in various land use change models in the literature, no studies attempted to integrate them. This DT-based CA model developed in this dissertation represents the first kind of such integration in land use change modeling. The coupled model would be able to handle a large set of driving factors and also avoid subjective bias when deriving the transition rules. The coupled model uses the cadastral parcel as a unit of analysis, which has practical policy implications because the responses of land use changes to various policy usually take place at the parcel level. Since parcel varies by their sizes and shapes, its use as a unit of analysis does make it difficult to apply CA, which initially designed to handle regular grid cells. This dissertation improves the treatment of the irregular cell in CA-based land use change models in literature by defining a cell\u27s neighborhood as a fixed distance buffer along the parcel boundary. The DT-based CA model was developed and validated in Hunterdon County, New Jersey. The data on historical land uses and various land use change driving factors for Hunterdon County were collected and processed using a Geographic Information System (GIS). Specifically, the county land uses in 1986, I995 and 2002 were overlaid with a parcel map to create parcel-based land use maps. The single land use in each parcel is based on a classification scheme developed thorough literature review and empirical testing in the study area. The possible land use status considered for each parcel is agriculture, barren land, forest, urban, water or wetlands following the land use/land cover classification by the New Jersey Department of Environment Protection. The identified driving factors for the future status of the parcel includes the present land use type, the number of soil restrictions to urban development, and the size of the parcel, the amount of wetlands within the parcel, the distribution of land uses in the neighborhood of the parcel, the distances to the nearest streams, urban centers and major roads. A set of transition rules illustrating the land use change processes during the period 1986-1995 were developed using a TD software J48 Classifier. The derived transition rules were applied to the 1995 land use data in a CA model Agent Analyst/RePast (Recursive Porous Agent Simulation Toolkit) to predict the spatial land use pattern in 2004, which were then validated by the actual land use map in 2002. The DT-based CA model had an overall accuracy of 84.46 percent in terms of the number of parcels and of 80.92 percent in terms of the total acreage in predicting land use changes. The model shows much higher capacity in predicting the quantitative changes than the locational changes in land use. The validated model was applied to simulate the 2011 land use patterns in Hunterdon County based on its actual land uses in 2002 under both business as usual and policy scenarios. The simulation results shows that successfully implementing current land use policies such as down zoning, open space and farmland preservation would prevent the total of 7,053 acres (741 acres of wetlands, 3,034 acres of agricultural lands, 250 acres of barren land, and 3,028 acres of forest) from future urban development in Hunterdon County during the period 2002-2011. The neighborhood of a parcel was defined by a 475-foot buffer along the parcel boundary in the study. The results of sensitivity analyses using two additional neighborhoods (237- and 712-foot buffers) indicate the insignificant impacts of the neighborhood size on the model outputs in this application