Using Agent-Based Simulation to Understand the Role of Values in Policy-Making

Perello-Moragues, Antoni; Noriega, Pablo

doi:10.1007/978-3-030-34127-5_35

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…Because digitalisation involves technologies that can provide more information to support decision-making in complex socio-technical problems, this leads inevitably to the need to clearly define the criteria and values beyond such decisions (Perello-Moragues and Noriega, 2020). For instance, if the virus is traced by sampling wastewater and this system is used to support lockdown de-escalation (i.e.…”

Section: Value-based Decision-makingmentioning

confidence: 99%

When the fourth water and digital revolution encountered COVID-19

Poch

Garrido-Baserba

Corominas

et al. 2020

Science of The Total Environment

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Section: Value-based Decision-makingmentioning

confidence: 99%

When the fourth water and digital revolution encountered COVID-19

Poch

Garrido-Baserba

Corominas

et al. 2020

Science of The Total Environment

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“…7,41,42 Some have focused on how health-related conditions or behaviors (e.g., obesity, 43 tobacco use 37 ) can spread through social networks. While system dynamics models and agent-based models (ABMs) have been used to study policy making in other fields such as agriculture and environmental policy, 44 none have done so in mental health 45 The current study seeks to address this knowledge gap.…”

Section: Policy Making Occurs In a Social Systemmentioning

confidence: 99%

Simulating the role of knowledge brokers in policy making in state agencies: An agent‐based model

Combs

Nelson

Luke

et al. 2022

Health Services Research

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Objective To model children's mental health policy making dynamics and simulate the impacts of knowledge broker interventions. Data sources Primary data from surveys (n = 221) and interviews (n = 64) conducted in 2019–2021 with mental health agency (MHA) officials in state agencies. Study design A prototype agent‐based model (ABM) was developed using the PARTE (Properties, Actions, Rules, Time, Environment) framework and informed through primary data collection. In each simulation, a policy is randomly generated (salience weights: cost, contextual alignment, and strength of evidence) and discussed among agents. Agents are MHA officials and heterogenous in their properties (policy making power and network influence) and policy preferences (based on salience weights). Knowledge broker interventions add agents to the MHA social network who primarily focus on the policy's research evidence. Data collection/extraction methods A sequential explanatory mixed method approach was used. Descriptive and regression analyses were used for the survey data and directed content analysis was used to code interview data. Triangulated results informed ABM development. In the ABM, policy makers with various degrees of decision influence interact in a scale‐free network before and after knowledge broker interventions. Over time, each decides to support or oppose a policy proposal based on policy salience weights and their own properties and interactions. The main outcome is an agency‐level decision based on policy maker support. Each intervention and baseline simulation runs 250 times across 50 timesteps. Principal findings Surveys and interviews revealed that barriers to research use could be addressed by knowledge brokers. Simulations indicated that policy decision outcomes varied by policy making context within agencies. Conclusions This is the first application of ABM to evidence‐informed mental health policy making. Results suggest that the presence of knowledge brokers can: (1) influence consensus formation in MHAs, (2) accelerate policy decisions, and (3) increase the likelihood of evidence‐informed policy adoption.

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“…Nevertheless, there are specific aspects of the design process that would need to address the role of values in designing artificial agents' architectures and behaviour. For instance, for an artificial agent that is intended to behave in an ethically-consistent manner, the engineer may commit to some cognitive architecture that includes values as an explicit and necessary construct in their inference-based decision-making models, or make explicit use of value theories that explain ethical behaviour without assuming rational ethical reasoners [24].…”

Section: Closing Remarksmentioning

confidence: 99%

“…It is work in progress (rather than a completed design methodology) which builds on a decade long research effort investigating online institutions and a conscientious design approach for building them successfully (e.g., [34] and see for example references in [1,20,22]). In addition to that long lasting interest, we draw also from experience from a different application of the framework: policy sandboxes, where some of the concepts and constructs involved in the heuristics we present here were first devised [24,25].…”

Section: Introductionmentioning

confidence: 99%

Design Heuristics for Ethical Online Institutions

Noriega¹,

Verhagen²,

Padget³

et al. 2022

Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV

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A major challenge in AI is designing autonomous systems that capture the values of stakeholders, and do so in such away that one can assess the extent to which that system's behaviour is aligned to those values. In this paper we discuss our response to this challenge that is both practical and built on clear principles. Specifically, we propose eleven heuristics to organise the process of making values operational in the design of particular class of AI systems called online institutions. These are governed systems of interacting communities of human and autonomous artificial agents.

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Using Agent-Based Simulation to Understand the Role of Values in Policy-Making

Abstract: We propose to explore the role of values in policy-making and the use of ABS for elucidating this role. In this paper we outline a conceptual framework for value-driven modelling of public policies and illustrate it with an agent-based simulation of irrigation practices.

Cited by 8 publications

References 42 publications

When the fourth water and digital revolution encountered COVID-19

When the fourth water and digital revolution encountered COVID-19

Simulating the role of knowledge brokers in policy making in state agencies: An agent‐based model

Design Heuristics for Ethical Online Institutions

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