Nicholas Mark Gotts

Bio: Nicholas Mark Gotts is an academic researcher from Macaulay Institute. The author has contributed to research in topics: Semantic grid & Ontology (information science). The author has an hindex of 18, co-authored 42 publications receiving 2184 citations. Previous affiliations of Nicholas Mark Gotts include James Hutton Institute.

Agent-based land-use models: a review of applications

Abstract: Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear-it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools.

Comparison of empirical methods for building agent-based models in land use science

Abstract: The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an ...

Agent-Based Simulation in the Study of Social Dilemmas

Abstract: This review discusses agent-based social simulation (ABSS) in relation to the study of social dilemmas such as the Prisoner's Dilemma and Tragedy of the Commons. Its aims are to explore the place of ABSS in relation to other research methods such as mathematical analysis, to familiarise artificial intelligence researchers (particularly those working on multi-agent systems) with a body of relevant multidisciplinary work, and to suggest directions for future ABSS research on social dilemmas. ABSS research can contribute greatly to the understanding of social phenomena, but needs to be based on a clear appreciation of the current `state of play' in the areas where it is used. With regard to `thin' (simple, general) simulation models, this primarily means attending to what has been or could be discovered by mathematical analysis, to work using other forms of simulation, and to the relevant theoretical disputes; with regard to `thick' (specific, detailed) models (about which the paper has less to say), linking to the relevant `thin' models and to the empirical evidence. The bulk of ABSS work on social dilemmas has been concentrated in quite a narrow – though certainly significant – area (reciprocal altruism in the Prisoner's Dilemma), and has sometimes been seriously flawed by over-ambitious claims, and insufficient attention to analytical approaches – although this same work has been very fertile in terms of inspiring further work, both analytical and simulation-based.

Representing and Reasoning with Qualitative Spatial Relations About Regions

Abstract: Qualitative Reasoning (QR) has now become a mature subfield of AI as its tenth annual international workshop, several books (e.g. (Weld and de Kleer, 1990; Faltings and Struss, 1992)) and a wealth of conference and journal publications testify. QR tries to make explicit our everyday commonsense knowledge about the physical world and also the underlying abstractions used by scientists and engineers when they create models. Given this kind of knowledge and appropriate reasoning methods, a computer could make predictions and diagnoses and explain the behavior of physical systems in a qualitative manner, even when a precise quantitative description is not available or is computationally intractable. Note that a representation is not normally deemed to be qualitative by the QR community simply because it is symbolic and utilizes discrete quantity spaces but because the distinctions made in these discretizations are relevant to high-level descriptions of the system or behavior being modeled.

Imitative versus nonimitative strategies in a land-use simulation

Abstract: This article describes results from a simulation model of rural land use, focusing on how the relative advantages of imitative and nonimitative approaches to land use selection change under different circumstances. It is shown that the success of "imitation" depends in quite complex ways on the type of imitation used, the strategies of other agents with which the imitator is interacting, and aspects of the heterogeneity of the environment.

Evolution and the Theory of Games

Abstract: In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Proceedings of the National Academy of Sciences

Abstract: where A represents the magnetic vector potential, is an integral of the hydromagnetic equations. This -integral made it possible to formulate a variational principle for the force-free magnetic fields. The integral expresses the fact that motions cannot transform a given field in an entirely arbitrary different field, if the conductivity of the medium isconsidered infinite. In this paper we shall show that the full set of hydromagnetic equations admit five more integrals, besides the energy integral, if dissipative processes are absent. These integrals, as we shall presently verify, are I2 =fbHvdV, (2)

Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review

Abstract: This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.