J Gareth Polhill
Dados Biográficos
| ID | 5774110 |
|---|---|
| NOME | J Gareth Polhill |
| PRENOMES | J Gareth |
| SOBRENOME | Polhill |
| ASSINATURA | POLHILL J G |
| AFILIAÇÕES | James Hutton Institute |
| VERIFICADO | Não |
| TOTAL DE OBRAS | 12 |
| TOTAL DE CITAÇÕES | 7 |
| OBRAS COMO AUTOR | 12 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2018 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 2 |
Calibrating a Global Trade Agent-Based Model with an HPC–ABC–SMC Framework
This study presents a High-Performance Computing and Approximate Bayesian Computation-Sequential Monte Carlo (HPC-ABC-SMC) framework for calibrating complex agent-based models.Applied to a global food trade model, the framework calibrates trade partner and trade volume objectives separately, using precomputed result mapping and task-level parallelisation to achieve up to a 42.1-fold computational efficiency improvement over traditional methods.Un…
Humat
In this paper we present HUMAT, an integrated modelling framework that supports the simulation of social dynamics.HUMAT integrates theoretical ideas about human motives, cognitive dissonance, individual and social decision strategies, and the processes of influence that take place in networked communities.As such, HUMAT provides a generic model that can be implemented in a wide variety of cases dealing with opinion and behaviour dynamics in a soc…
Simulating the Role of Norms in Processes of Social Innovation
Norms and values are critical drivers in social innovation processes, such as community projects on sustainable energy. Simulating such processes could help uncover conditions that support these social innovations. Capturing the rich literature on drivers of social innovation in more simple computational rules is a challenge however. In this paper, we present three empirically grounded case simulations addressing social innovations where norms an…
Agent-based modelling as a method for prediction in complex social systems
Agent-based models (ABMs) have their origins in considerations of complexity science stipulating that many phenomena can be ‘grown from the bottom up’. Explicitly, this was expressed in Epstein & Axtell’s (1996) Growing Artificial Societies as the change from ‘Can you explain it?’ to ‘Can you grow it?’. In 2008, Epstein published an article entitled Why Model? in which he discussed his exasperation with people asking for predictions from ABM, poi…
Calibrating Agent-Based Models Using Uncertainty Quantification Methods
Agent-based models (ABMs) can be found across a number of diverse application areas ranging from simulating consumer behaviour to infectious disease modelling. Part of their popularity is due to their ability to simulate individual behaviours and decisions over space and time. However, whilst there are plentiful examples within the academic literature, these models are only beginning to make an impact within policy areas. Whilst frameworks such a…
Sensemaking of causality in agent-based models
Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of integrating diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level component of emergent phenomena and recognize the important role of single-level causes without limiting the scope of th…
Using Agent-Based Models for Prediction in Complex and Wicked Systems
This paper uses two thought experiments to argue that the complexity of the systems to which agentbased models (ABMs) are o en applied is not the central source of di iculties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. 'Wickedness' is…
The ODD Protocol for Describing Agent-Based and Other Simulation Models
by Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gareth Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón
Computational Models That Matter During a Global Pandemic Outbreak
The COVID-pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers' demands for advice despite the absence of adequate, tr…
Modelling food security
Exploring sustainable scenarios in debt-based social–ecological systems
Too much of a good thing? Using a spatial agent-based model to evaluate “unconventional” workplace sharing programmes
Sensemaking of causality in agent-based models
Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of integrating diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level component of emergent phenomena and recognize the important role of single-level causes without limiting the scope of th…
Modelling food security
Too much of a good thing? Using a spatial agent-based model to evaluate “unconventional” workplace sharing programmes
Agent-based modelling as a method for prediction in complex social systems
Agent-based models (ABMs) have their origins in considerations of complexity science stipulating that many phenomena can be ‘grown from the bottom up’. Explicitly, this was expressed in Epstein & Axtell’s (1996) Growing Artificial Societies as the change from ‘Can you explain it?’ to ‘Can you grow it?’. In 2008, Epstein published an article entitled Why Model? in which he discussed his exasperation with people asking for predictions from ABM, poi…
Too much of a good thing? Using a spatial agent-based model to evaluate “unconventional” workplace sharing programmes
The ODD Protocol for Describing Agent-Based and Other Simulation Models
by Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gareth Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón
Computational Models That Matter During a Global Pandemic Outbreak
The COVID-pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers' demands for advice despite the absence of adequate, tr…
Modelling food security
Exploring sustainable scenarios in debt-based social–ecological systems
Using Agent-Based Models for Prediction in Complex and Wicked Systems
This paper uses two thought experiments to argue that the complexity of the systems to which agentbased models (ABMs) are o en applied is not the central source of di iculties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. 'Wickedness' is…
Calibrating Agent-Based Models Using Uncertainty Quantification Methods
Agent-based models (ABMs) can be found across a number of diverse application areas ranging from simulating consumer behaviour to infectious disease modelling. Part of their popularity is due to their ability to simulate individual behaviours and decisions over space and time. However, whilst there are plentiful examples within the academic literature, these models are only beginning to make an impact within policy areas. Whilst frameworks such a…
Sensemaking of causality in agent-based models
Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of integrating diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level component of emergent phenomena and recognize the important role of single-level causes without limiting the scope of th…
Agent-based modelling as a method for prediction in complex social systems
Agent-based models (ABMs) have their origins in considerations of complexity science stipulating that many phenomena can be ‘grown from the bottom up’. Explicitly, this was expressed in Epstein & Axtell’s (1996) Growing Artificial Societies as the change from ‘Can you explain it?’ to ‘Can you grow it?’. In 2008, Epstein published an article entitled Why Model? in which he discussed his exasperation with people asking for predictions from ABM, poi…
Simulating the Role of Norms in Processes of Social Innovation
Norms and values are critical drivers in social innovation processes, such as community projects on sustainable energy. Simulating such processes could help uncover conditions that support these social innovations. Capturing the rich literature on drivers of social innovation in more simple computational rules is a challenge however. In this paper, we present three empirically grounded case simulations addressing social innovations where norms an…
Humat
In this paper we present HUMAT, an integrated modelling framework that supports the simulation of social dynamics.HUMAT integrates theoretical ideas about human motives, cognitive dissonance, individual and social decision strategies, and the processes of influence that take place in networked communities.As such, HUMAT provides a generic model that can be implemented in a wide variety of cases dealing with opinion and behaviour dynamics in a soc…
Calibrating a Global Trade Agent-Based Model with an HPC–ABC–SMC Framework
This study presents a High-Performance Computing and Approximate Bayesian Computation-Sequential Monte Carlo (HPC-ABC-SMC) framework for calibrating complex agent-based models.Applied to a global food trade model, the framework calibrates trade partner and trade volume objectives separately, using precomputed result mapping and task-level parallelisation to achieve up to a 42.1-fold computational efficiency improvement over traditional methods.Un…
Computer Science (9 obras) · Artificial Intelligence (4 obras) · Business (4 obras) · Epistemology (4 obras) · Economics (3 obras) · Management science (3 obras) · Opinion Dynamics and Social Influence (3 obras) · Sociology (3 obras) · Biology (2 obras) · Complex Systems and Time Series Analysis (2 obras)