Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change

We present a new bilateral agent-based land market model, which moves beyond previous work by explicitly modeling behavioral drivers of land-market transactions on both the buyer and seller sides; formation of bid prices (of buyers) and ask prices (of sellers); and the relative division of the gains from trade from the market transactions. We analyze model output using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression models. We first demonstrate that our model replicates relevant theoretical results of the traditional Alonso/Von Thünen model (structural validation). We then explore how urban morphology and land rents change as the relative market power of buyers and sellers changes (i.e., we move from a 'sellers' market' to a 'buyers' market'). We demonstrate that these strategic price dynamics have differential effects on land rents, but both lead to increased urban expansion

Agent-Based Urban Land Markets: Agent\'s Pricing Behavior, Land Prices and Urban Land Use Change

We present a new bilateral agent-based land market model, which moves beyond previous work by explicitly modeling behavioral drivers of land-market transactions on both the buyer and seller side; formation of bid prices (of buyers) and ask prices (of sellers); and the relative division of the gains from trade from the market transactions. We analyze model output using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression models. We first demonstrate that our model replicates relevant theoretical results of the traditional Alonso/Von Th�nen model (structural validation). We then explore how urban morphology and land rents change as the relative market power of buyers and sellers changes (i.e., we move from a \'sellers\' market\' to a \'buyers\' market\'). We demonstrate that these strategic price dynamics have differential effects on land rents, but both lead to increased urban expansion.Location Choice, Urban Land Market, Agent-Based Computational Economics, Land Use, Land Rent Gradient, Spatial Simulation

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

The agricultural policy simulator (AgriPoliS): an agent-based model to study structural change in agriculture (Version 1.0)

A central criticism common to agricultural economic modelling approaches for policy analysis is that they do not adequately take account of a number of characteristic factors of the agricultural sector. This concerns aspects like the immobility of land, heterogeneity of farms, interactions between farms, space, dynamic adjustment processes as well as dynamics of structural change. In brief, modelling the complexity of the system has not been at the centre of interest. In terms of modelling complex economic systems, an agent-based modelling approach is a suitable approach to quantitatively model and understand such systems in a more natural way. In the same way, this applies to the modelling of agricultural structures. In particular, agent-based models of agricultural structures allow for carrying out computer experiments to support a better understanding of the complexity of agricultural systems, structural change, and endogenous adjustment reactions in response to a policy change. This paper presents the agent-based model AgriPoliS (Agricultural Policy Simulator) which simultaneously considers a large number of individually acting farms, product markets, investment activity, as well as the land market, and a simple spatial representation. The ultimate objective of AgriPoliS is to study the interrelationship of rents, technical change, product prices, investments, production and policies, structural effects resulting from these, the analysis of the winners and losers of agricultural policy as well as the costs and efficiency of various policy measures. -- G E R M A N V E R S I O N: Ein oft genannter Kritikpunkt an vielen agrarökonomischen Politikanalysemodellen ist, dass diese nur ungenügend Bezug nehmen auf Aspekte wie die Immobilität von Boden, Heterogenität der Akteure, Interaktionen zwischen Betrieben, räumliche Bezüge, dynamische Anpassungsprozesse und Strukturwandel. Kurz, die Modellierung komplexer Wirkungszusammenhänge steht weniger oder nicht im Zentrum des Interesses. Agentenbasierte Modelle stellen einen Weg dar, das Verständnis komplexer ökonomischer Zusammenhänge zu verbessern bzw. zu quantifizieren. Insbesondere erlauben sie die Durchführung von einer Vielzahl von Computerexperimenten, mit denen Fragestellungen wie der Zusammenhang zwischen Politikmaßnahmen und Strukturwandel untersucht werden können. Basierend darauf, stellt dieser Beitrag das agentenbasierte Modell AgriPoliS (Agricultural Policy Simulator) vor. AgriPoliS ist ein räumlich-dynamisches Modell einer Agrarstruktur, in dem eine Vielzahl individuell abgebildeter landwirtschaftlicher Unternehmen in einer vereinfacht dargestellten Agrarregion agiert und beispielsweise um begrenzt verfügbare landwirtschaftliche Flächen konkurriert.Agent-based systems,Multi-agent systems,Policy analysis,Structural change,Simulation,Agentenbasierte Systeme,Politikanalyse,Multi-Agentensysteme,Strukturwandel,Simulation

Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.