Modeling the Use of Nonrenewable Resources Using a Genetic Algorithm

This paper shows, how a genetic algorithm (GA) can be used to model an economic process: the interaction of profit-maximizing oil-exploration firms that compete with each other for a limited amount of oil. After a brief introduction to the concept of multi-agent-modeling in economics, a GA-based resource-economic model is developed. Several model runs based on different economic policy assumptions are presented and discussed in order to show how the GA-model can be used to gain insight into the dynamic properties of economic systems. The remainder outlines deficiencies of GA-based multi-agent approaches and sketches how the present model can be improved.

Exploring auction based energy trade with the support of MAS and blockchain technology

This document describes a simulation of the local energy market with support of multi-agent approach and blockchain technology. The investigated points include blockchain technology and its applications, Ethereum platform and smart contracts as a tool for storing data of operations and creating assets, multi-agent approach to model the local energy market. The document explores building a solution for proposed problem with blockchain technology, agent interactions on the simulated market and auction models, that provide sustainability and profit for the local energy market overall

Spatial Dynamic Modeling and Urban Land Use Transformation:

Assessing the economic impacts of urban land use transformation has become complex and acrimonious. Although community planners are beginning to comprehend the economic trade-offs inherent in transforming the urban fringe, they find it increasingly difficult to analyze and assess the trade-offs expediently and in ways that can influence local decisionmaking. New and sophisticated spatial modeling techniques are now being applied to urban systems that can quickly assess the probable spatial outcomes of given communal policies. Applying an economic impact assessment to the probable spatial patterns can provide to planners the tools needed to quickly assess scenarios for policy formation that will ultimately help inform decision makers. This paper focuses on the theoretical underpinnings and practical application of an economic impact analysis submodel developed within the Land use Evolution and Impact Assessment Modeling (LEAM) environment. The conceptual framework of LEAM is described, followed by an application of the model to the assessment of the cost of urban sprawl in Kane County, Illinois. The results show the effectiveness of spatially explicit modeling from a theoretical and a practical point of view. The agent-based approach of spatial dynamic modeling with a high spatial resolution allows for discerning the macro-level implications of micro-level behaviors. These phenomena are highlighted in the economic submodel in the discussion of the implications of land use change decisions on individual and communal costs; low-density development patterns favoring individual behaviors at the expense of the broader community.

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

A Note on the Equivalence of Rationalizability Concepts in Generalized Nice Games

Moulin (1984) describes the class of nice games for which the solution concept of point-rationalizability coincides with iterated elimination of strongly dominated strategies. As a consequence nice games have the desirable property that all rationalizability concepts determine the same strategic solution. However, nice games are characterized by rather strong assumptions. For example, only single-valued best responses are admitted and the individual strategy sets have to be convex and compact subsets of the real line R1. This note shows that equivalence of all rationalizability concepts can be extended to multi-valued best response correspondences. The surprising finding is that equivalence does not hold for individual strategy sets that are compact and convex subsets of Rn with n>1.

Modeling the use of nonrenewable resources using a genetic algorithm

This paper shows, how a genetic algorithm (GA) can be used to model an economic process: the interaction of profit-maximizing oil-exploration firms that compete with each other for a limited amount of oil. After a brief introduction to the concept of multi-agent-modeling in economics, a GA-based resource-economic model is developed. Several model runs based on different economic policy assumptions are presented and discussed in order to show how the GA-model can be used to gain insight into the dynamic properties of economic systems. The remainder outlines deficiencies of GA-based multi-agent approaches and sketches how the present model can be improved

Pricing Chinese rain: a multi-site multi-period equilibrium pricing model for rainfall derivatives

Many industries are exposed to weather risk which they can transfer on financial markets via weather derivatives. Equilibrium models based on partial market clearing became a useful tool for pricing such kind of financial instruments. In a multi-period equilibrium pricing model agents rebalance their portfolio of weather bonds and a risk free asset in each period such that they maximize the expected utility of their incomes constituted by possibly weather dependent profits and payoffs of portfolio positions. We extend the model to a multisite version and apply it to pricing rainfall derivatives for Chinese provinces. By simulating realistic market conditions with two agent types, farmers with profits highly exposed to weather risk and a financial investor diversifying her financial portfolio, we obtain equilibrium prices for weather derivatives on cumulative monthly rainfall. Dynamic portfolio optimization under market clearing and utility indifference of these representative agents determines equilibrium quantity and price for rainfall derivatives.rainfall derivatives, equilibrium pricing, space-time Markov model

Buy, Sell or Rent the Farm: An Agent Based Simulation of Farm Succession and Land Valuation

The impact of widespread farm ownership by large investors in Canada could be influential and remains uncertain. In fact, there are sound financial reasons for buying farmland as an investment, including diversification benefits available to investment portfolios. Studies have found that the correlation between farmland price yield and the yields of major financial assets, such as stocks, bonds and real estate, are consistently negative. On the other hand, the prime objectives of many farm family businesses are “to maintain control and pass on a secure and sound business to the next generation” (Hay and Morris 1984, Errington 2002). In Western Canada, farmland is generally retained within the family by succession because of strong emotional and economic linkages. However, very little prior research has examined the long-term effect of interactions between these two options for Prairie farmland transition. Due to the complexity of the problem, the agent based simulation model (ABSM) is one of the few feasible ways for this issue. The simulation model developed in this study builds upon Anderson’s (2012) work simulating farming activity in Canadian Agricultural Region 1A in Saskatchewan, using the Repast© software. Two extensions or modules for the farm simulation are developed in this thesis, comprising farm succession as well as the presence of institutional investors who purchase farmland as a financial asset in order to diversify aggregate risk in their portfolio. Thirty years of farming and investing performance are simulated in four different scenarios to examine the effects of various levels of institutional investor participation. Institutional investors are found to elevate farmland prices from between 15% to 40% across different scenarios, while farmers ultimately tend to lease slightly more land to compensate and expand their farms. Meanwhile, the total number of farms in the region fall over time, while larger individual farms emerge over the simulated period, both with or without investors. Based on this simulated evidence, we conclude that the overall impact of institutional investors on future farming will be subtle, and continued farm success here is contingent on farmers being willing to rely more on rental land for farm expansion