Gary Polhill
Biographic Data
| ID | 4817062 |
|---|---|
| NAME | Gary Polhill |
| GIVEN NAMES | Gary |
| FAMILY NAME | Polhill |
| SIGNATURE | POLHILL G |
| AFFILIATIONS | James Hutton Institute |
| ORCID | 0000-0002-8596-0590 |
| VERIFIED | Yes |
| TOTAL WORKS | 23 |
| TOTAL CITATIONS | 9 |
| WORKS AS AUTHOR | 23 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
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…
Tackling the challenge of interdisciplinary energy research
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
Simulating the Actions of Commuters Using a Multi-Agent System
The activity of commuting to and from a place of work a ects not only those travelling but also wider society through their contribution to congestion and pollution. It is desirable to have a means of simulating commuting in order to allow organisations to predict the e ects of changes to working patterns and locations and inform decision making. In this paper we outline an agent-based so ware framework that combines realworld data from multiple …
Too much of a good thing? Using a spatial agent-based model to evaluate “unconventional” workplace sharing programmes
Experiments with a Model of Domestic Energy Demand
The CEDSS-. agent-based model of domestic energy demand at community level is described. CEDSS (Community Energy Demand Social Simulator) is focused on household decisions (the model's agents are households) to buy energy-using appliances, heating systems, and insulation, over the period from to . Its empirical basis is a survey of households in Aberdeen and Aberdeenshire, Scotland, carried out in , combined with publicly available data on househ…
Exploring the Combined Effect of Factors Influencing Commuting Patterns and CO2 Emissions in Aberdeen Using an Agent-Based Model
This paper develops an agent-based model of the daily commute in Aberdeen City and the surrounding area in Scotland, UK. We study the impact of flexitime work arrangements, urban concentration, a new bypass, and cycle lanes on commute time length, reliability and CO 2 emissions, and analyse the diverse conflation of these factors and the di erent connections of them in order to detect their cumulative e ects. Our results suggest that flexitime wi…
Extracting OWL Ontologies from Agent-Based Models
Using OWL ontologies to represent the state and structure of a simulation at any one time has been argued to improve the transparency of a social simulation, on the basis that this information is then not embedded in the source code of the model, or in the computer's memory at run-time. Should transparency of such a form be desirable, it would be preferable to enable it by extracting the information automatically from a running model. However, se…
The Complexities of Agent-Based Modeling Output Analysis
The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the i…
Combining Diverse Data Sources for Cedss, an Agent-Based Model of Domestic Energy Demand
CEDSS (Community Energy Demand Social Simulator) is an empirical agent-based model designed and built as part of a multi-method social science project investigating the determinants of domestic energy demand. Ideally, empirical modellers, within and beyond social simulation, would prefer to work from an integrated dataset, gathered for the purposes of developing the model. In practice, many have to work with less than ideal data, often including …
The ODD protocol
Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change
This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable …
ODD Updated
An update to Volker Grimm and colleagues' Overview, Design concepts and Details (ODD) protocol for documenting individual and agent based models (I/ABM) has recently been published in Ecological Modelling. This renames the 'State variables and scales' element to 'Entities, state variables and scales', and the 'Input' element to 'Input data', introduces two new Design concepts ('Basic principles' and 'Learning'), and renames another ('Fitness' is …
E-Social Science and Evidence-Based Policy Assessment
The PolicyGrid project is exploring the role of Grid, Semantic Web, and Web 2.0 technologies to support e-Social Science, with particular emphasis on tools to facilitate evidence-based policy making. In this article, we discuss the challenges associated with construction of a provenance framework to support evidence-based policy assessment. We then discuss ourSpaces, a virtual research environment for e-Social Science that uses the Web 2.0 paradi…
Agent-based land-use models
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…
Tackling the challenge of interdisciplinary energy research
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…
Agent-based land-use models
E-Social Science and Evidence-Based Policy Assessment
The PolicyGrid project is exploring the role of Grid, Semantic Web, and Web 2.0 technologies to support e-Social Science, with particular emphasis on tools to facilitate evidence-based policy making. In this article, we discuss the challenges associated with construction of a provenance framework to support evidence-based policy assessment. We then discuss ourSpaces, a virtual research environment for e-Social Science that uses the Web 2.0 paradi…
The ODD protocol
Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change
This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable …
ODD Updated
An update to Volker Grimm and colleagues' Overview, Design concepts and Details (ODD) protocol for documenting individual and agent based models (I/ABM) has recently been published in Ecological Modelling. This renames the 'State variables and scales' element to 'Entities, state variables and scales', and the 'Input' element to 'Input data', introduces two new Design concepts ('Basic principles' and 'Learning'), and renames another ('Fitness' is …
Combining Diverse Data Sources for Cedss, an Agent-Based Model of Domestic Energy Demand
CEDSS (Community Energy Demand Social Simulator) is an empirical agent-based model designed and built as part of a multi-method social science project investigating the determinants of domestic energy demand. Ideally, empirical modellers, within and beyond social simulation, would prefer to work from an integrated dataset, gathered for the purposes of developing the model. In practice, many have to work with less than ideal data, often including …
Extracting OWL Ontologies from Agent-Based Models
Using OWL ontologies to represent the state and structure of a simulation at any one time has been argued to improve the transparency of a social simulation, on the basis that this information is then not embedded in the source code of the model, or in the computer's memory at run-time. Should transparency of such a form be desirable, it would be preferable to enable it by extracting the information automatically from a running model. However, se…
The Complexities of Agent-Based Modeling Output Analysis
The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the i…
Exploring the Combined Effect of Factors Influencing Commuting Patterns and CO2 Emissions in Aberdeen Using an Agent-Based Model
This paper develops an agent-based model of the daily commute in Aberdeen City and the surrounding area in Scotland, UK. We study the impact of flexitime work arrangements, urban concentration, a new bypass, and cycle lanes on commute time length, reliability and CO 2 emissions, and analyse the diverse conflation of these factors and the di erent connections of them in order to detect their cumulative e ects. Our results suggest that flexitime wi…
Experiments with a Model of Domestic Energy Demand
The CEDSS-. agent-based model of domestic energy demand at community level is described. CEDSS (Community Energy Demand Social Simulator) is focused on household decisions (the model's agents are households) to buy energy-using appliances, heating systems, and insulation, over the period from to . Its empirical basis is a survey of households in Aberdeen and Aberdeenshire, Scotland, carried out in , combined with publicly available data on househ…
Too much of a good thing? Using a spatial agent-based model to evaluate “unconventional” workplace sharing programmes
Simulating the Actions of Commuters Using a Multi-Agent System
The activity of commuting to and from a place of work a ects not only those travelling but also wider society through their contribution to congestion and pollution. It is desirable to have a means of simulating commuting in order to allow organisations to predict the e ects of changes to working patterns and locations and inform decision making. In this paper we outline an agent-based so ware framework that combines realworld data from multiple …
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…
Tackling the challenge of interdisciplinary energy research
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…
Computer Science (20 works) · Data science (8 works) · Engineering (8 works) · Artificial Intelligence (7 works) · Management science (7 works) · Business (6 works) · Economics (6 works) · Sociology (5 works) · Ecology (4 works) · Epistemology (4 works)