55 research outputs found

    Simulating extortees: group structures and reasoning modes

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    Extortion is a unique crime in that it involves a long-term interaction between the victim and the perpetrator. It is also an interesting crime in that it seems to afflict whole communities, cities or even countries. Extortion is often modelled as an interdependent choice between extorter and extortee using a game theoretic framework. Although a game theoretic model takes into account the first uniqueness of a long-term relationship but leaves out the social influence factors that can make extortion endemic within a social group or society. In this paper we present an agent-based model which looks at the decision making of extortees from a social perspective, transforming the traditional extortion game into a collective problem

    Construct Validity and Theoretical Embeddedness of Agent-based Models of Normative Behaviour

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    In this paper we assess the construct validity and theoretical emdeddedness of agent-based models of normative behaviour drawing on experimental social psychology. We contend that social psychology and agent-based modelling share the focus of 'observing' the processes and outcomes of the interaction of individual agents. The paper focuses on two from a taxonomy of agent-based models of normative behaviour. This enables the identification of the assumptions the models are built on and in turn, reflection on the assumptions themselves from a socio-psychological perspective

    Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support

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    Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns

    Co-learning through participatory evaluation: an example using Theory of Change in a large-scale EU-funded tourism intervention

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    Tourism interventions, as tools for social change and preservation of natural and cultural assets are inherently complex. This study presents an improved method for the evaluation of complex tourism interventions. We argue that participatory methods can promote a culture of evaluation that supports partners throughout evidencing project impacts, eliminating negative attitudes to evaluation resulting from fear of being judged on performance. We demonstrate that Theory of Change (ToC) is an effective tool that allows organisations to actively co-create and own an evaluation strategy to ensure the delivery of project outcomes. We show how ToC can be applied as a useful process and impact evaluation tool. This paper represents a novel methodological application of ToC based on participatory approaches to evaluation to disseminate knowledge and to improve decision-making in the field of tourism interventions and tourism policy making

    Climate mitigation and adaptation action in the UK and devolved nations - A typology

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    Typology document generated by policy review to inform a systematic review search strateg

    Simulating extortees: group structures and reasoning modes

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    Extortion is a unique crime in that it involves a long-term interaction between the victim and the perpetrator. It is also an interesting crime in that it seems to afflict whole communities, cities or even countries. Extortion is often modelled as an interdependent choice between extorter and extortee using a game theoretic framework. Although a game theoretic model takes into account the first uniqueness of a long-term relationship but leaves out the social influence factors that can make extortion endemic within a social group or society. In this paper we present an agent-based model which looks at the decision making of extortees from a social perspective, transforming the traditional extortion game into a collective problem

    Instinct for detection

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