The implications of alternative developer decision-making strategies on land-use and land-cover in an agent-based land market model

Land developers play a key role in land-use and land cover change, as\ud they directly make land development decisions and bridge the land and housing\ud markets. Developers choose and purchase land from rural land owners, develop\ud and subdivide land into parcel lots, build structures on lots, and sell houses to residential households. Developers determine the initial landscaping states of developed parcels, affecting the state and future trajectories of residential land cover, as well as land market activity. Despite their importance, developers are underrepresented in land use change models due to paucity of data and knowledge regarding their decision-making. Drawing on economic theories and empirical literature, we have developed a generalized model of land development decision-making within a broader agent-based model of land-use change via land markets. Developer’s strategies combine their specialty in developing of particular subdivision types, their perception of and attitude towards market uncertainty, and their learning and adaptation strategies based on the dynamics of the simulated land and housing markets. We present a new agent-based land market model that includes these elements. The model will be used to experiment with these different development decision-making methods and compare their impacts on model outputs, particularly on the quantity and spatial pattern of resultant land use changes. Coupling between the land market and a carbon sequestration model, developed for the larger SLUCE2 project, will allow us, in future work, to examine how different developer’s strategies will affect the carbon balance in residential\ud landscape

The Repast Simulation/Modelling System for Geospatial Simulation

The use of simulation/modelling systems can simplify the implementation of agent-based models. Repast is one of the few simulation/modelling software systems that supports the integration of geospatial data especially that of vector-based geometries. This paper provides details about Repast specifically an overview, including its different development languages available to develop agent-based models. Before describing Repast’s core functionality and how models can be developed within it, specific emphasis will be placed on its ability to represent dynamics and incorporate geographical information. Once these elements of the system have been covered, a diverse list of Agent-Based Modelling (ABM) applications using Repast will be presented with particular emphasis on spatial applications utilizing Repast, in particular, those that utilize geospatial data

Introducing Preference Heterogeneity into a Monocentric Urban Model: an Agent-Based Land Market Model

This paper presents an agent-based urban land market model. We first replace the centralized price determination mechanism of the monocentric urban market model with a series of bilateral trades distributed in space and time. We then run the model for agents with heterogeneous preferences for location. Model output is analyzed using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression. We demonstrate that heterogeneity in preference for proximity alone is sufficient to generate urban expansion and that information on agent heterogeneity is needed to fully explain land rent variation over space. Our agent-based land market model serves as computational laboratory that may improve our understanding of the processes generating patterns observed in real-world data

Quantitative modelling of the human–Earth System a new kind of science?

The five grand challenges set out for Earth System Science by the International Council for Science in 2010 require a true fusion of social science, economics and natural science—a fusion that has not yet been achieved. In this paper we propose that constructing quantitative models of the dynamics of the human–Earth system can serve as a catalyst for this fusion. We confront well-known objections to modelling societal dynamics by drawing lessons from the development of natural science over the last four centuries and applying them to social and economic science. First, we pose three questions that require real integration of the three fields of science. They concern the coupling of physical planetary boundaries via social processes; the extension of the concept of planetary boundaries to the human–Earth System; and the possibly self-defeating nature of the United Nation’s Millennium Development Goals. Second, we ask whether there are regularities or ‘attractors’ in the human–Earth System analogous to those that prompted the search for laws of nature. We nominate some candidates and discuss why we should observe them given that human actors with foresight and intentionality play a fundamental role in the human–Earth System. We conclude that, at sufficiently large time and space scales, social processes are predictable in some sense. Third, we canvass some essential mathematical techniques that this research fusion must incorporate, and we ask what kind of data would be needed to validate or falsify our models. Finally, we briefly review the state of the art in quantitative modelling of the human–Earth System today and highlight a gap between so-called integrated assessment models applied at regional and global scale, which could be filled by a new scale of model

PUMA - a multi-agent model of urban systems

It is increasingly recognised that land use change processes are the outcome of decisions made by individual actors, such as land owners, authorities, firms and households. As multi-agent models provide a natural framework for modelling urban processes on the level of individual actors, Utrecht University, Eindhoven University of Technology and RIVM are developing PUMA (Predicting Urbanisation with Multi-Agents), a full fledged multi-agent system of urban processes. PUMA consists of various modules, representing the behaviours of specific actors. The land conversion module describes farmers', authorities', investors' and developers' decisions to sell or buy land and develop it into other uses. The households module describes households' housing careers in relation to life cycle events (marriage, child birth, aging, job change etc.). The firms module includes firms' demography and their related demand for production facilities leading to location choice processes. The daily activity pattern module describes the trips made and locations visited by individuals to carry out certain tasks. This module generates aggregated effects of individual behaviours (congestion, pollution, noise), affecting households' or firms' longer term location decisions. The paper describes the model system architecture and the interactions between the modules. Particular attention is devoted to the households module that includes a behaviourally sophisticated model of households' process of awakening (deciding to actively search for another dwelling), search and acceptance of an offered dwelling. This model was calibrated on the Dutch Housing Preferences Survey. Based on the disaggregate housing search and acceptance model, the households module describes housing market dynamics and indicates the demand for new dwellings per region. The paper describes the model specification and calibration in detail. The households module was implemented and tested for the Northwing of the Dutch Randstad, including about 1.5 million households and 1.6 million dwellings. The paper describes the implementation and the first model results.

Analyzing the Dynamics of Relative Prices on a Market with Speculative and Non-Speculative Agents Based on the Evolutionary Model

The paper deals with an evolutionary model focused on the relation between the behavior of prices and the structure of the population of economic agents. The model allows for identification of the short-term behavior of prices and the dynamics of the population of economic agents in the context of seven scenarios. These scenarios are a combination of four key factors: market regulations, the maturity of the market; the intervention of the state on the market supply side and the modifications of the incentives to speculate and not-speculate. The main findings of the simulation of the scenarios are: i) The presence of speculators leave long lasting effects which do not die out with the decrease in the number of speculators; ii) In the presence of high speculations the intervention of the state can act as an anchor to the market helping to lower the prices; iii) The market forces have a more lasting effect than the state regulation mechanisms.relative prices, speculative and non-speculative agents, evolutionary model

Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models

National innovation systems, developing countries, and the role of intermediaries: a critical review of the literature

Developed over the past three decades, the national innovation system concept (NIS) has been widely used by both scholars and policy makers to explain how interactions between a set of distinct, nationally bounded institutions supports and facilitates technological change and the emergence and diffusion of new innovations. This concept provides a framework by which developing countries can adopt for purposes of catching up. Initially conceived on structures and interactions identified in economically advanced countries, the application of the NIS concept to developing countries has been gradual and has coincided – in the NIS literature – with a move away from overly macro-interpretations to an emphasis on micro-level interactions and processes, with much of this work questioning the nation state as the most appropriate level of analysis, as well as the emergence of certain intermediary actors thought to facilitate knowledge exchange between actors and institutions. This paper reviews the NIS literature chronologically, showing how this shift in emphasis has diminished somewhat the importance of both institutions, particularly governments, and the process of institutional capacity building. In doing so, the paper suggests that more recent literature on intermediaries such as industry associations may offer valuable insights to how institutional capacity building occurs and how it might be directed, particularly in the context of developing countries where governance capacities are often lacking, contributing to less effective innovation systems, stagnant economies, and unequal development

Towards a Co-Evolutionary Model of Demographics and Infrastructure

National infrastructure systems provide a foundation for economic prosperity and well-being. In addition to factors such as technological change and obsolescence, infrastructure systems need to respond to changing levels of demand, which is strongly driven by population growth. However demographic change is not independent of economic conditions, or the nature and quality of infrastructure. This research is concerned with the interrelationships between demographics, economy and infrastructure. The paper therefore develops a novel approach to modelling the evolution of a national economy in the context of changing demographics and infrastructure provision. This approach is based in a model with coupled sub-systems which are spatially disaggregate with explicit temporal dynamics. A version of the model is calibrated using a demographic component which incorporates both natural change and migration, and an economic model which recognises both labour and capital as factors of production. Infrastructure is present as an influence on accessibility, geographical attractiveness and economic productivity. The performance of the model is explored through a variety of scenarios which are offered as an initial proof of concept of the feasibility of implementing a co-evolutionary model of demographic and economic growth over a medium to long time horizon. These scenarios indicate the influence of government policies for international migration and infrastructure investment on regional development and performance