Agent-Based Modeling as a Legal Theory Tool

Benthall, Sebastian; Strandburg, Katherine J.

doi:10.3389/fphy.2021.666386

Cited by 4 publications

(2 citation statements)

References 60 publications

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“…Typically, this is done via the agent-based model [20,21]. As discussed by [22] the use of agent-based models has dramatically increased over the past 20 years, however they remain largely absent from the jurisprudential literature. As highlighted by the examples discussed by Benthall and Stranburg, when law and agent-based models do collide it is largely in the space of regulation and policy analysis or, more generally, a topic within law and economics.…”

Section: Generative Numeric Methodsmentioning

confidence: 99%

Jurisprudence Meets Physics

Koehler

2022

Front. Phys.

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For many system level questions jurisprudential data has grown to a size and scale that no longer lends itself to traditional analytic techniques driven by human examination and direct analysis. While there will always be vast numbers of specific questions well within the capabilities of humans, an understanding of the system as a whole is no longer among them. Over the past several decades jurisprudence has begun to use mathematical and other analytic techniques many of which were developed in the physical sciences. It is now time for jurisprudence to embrace more fully the analytic tools of these other disciplines, specifically those coming out of physics, in order to continue to produce new insights to aid in the structure, function, design of judicial systems and the analysis of judicial dynamics.

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Section: Generative Numeric Methodsmentioning

confidence: 99%

Jurisprudence Meets Physics

Koehler

2022

Front. Phys.

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“…ABM is being used to inform policy or decisions in various contexts. Recent examples include land use and agricultural policy (Dai et al, 2020), ecosystems and natural resource management (González et al, 2018), control of epidemics (Kerr et al, 2021;Truszkowska et al, 2021), economic policy (Chersoni et al, 2022;Dosi et al, 2020), institutional design (Benthall & Strandburg, 2021), and technology diffusion (Beretta et al, 2018). Moreover, ABM rely on the idea that information does not flow freely and homogeneously within systems and they often connect the policy domain to the field of network science (Kenis & Schneider, 2019).…”

Section: Existing Literaturementioning

confidence: 99%

Modelling Complexity with Unconventional Data: Foundational Issues in Computational Social Science

Fontana

Guerzoni

2022

Handbook of Computational Social Science for Policy

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The large availability of data, often from unconventional sources, does not call for a data-driven and theory-free approach to social science. On the contrary, (big) data eventually unveil the complexity of socio-economic relations, which has been too often disregarded in traditional approaches. Consequently, this paradigm shift requires to develop new theories and modelling techniques to handle new types of information. In this chapter, we first tackle emerging challenges about the collection, storage, and processing of data, such as their ownership, privacy, and cybersecurity, but also potential biases and lack of quality. Secondly, we review data modelling techniques which can leverage on the new available information and allow us to analyse relationships at the microlevel both in space and in time. Finally, the complexity of the world revealed by the data and the techniques required to deal with such a complexity establishes a new framework for policy analysis. Policy makers can now rely on positive and quantitative instruments, helpful in understanding both the present scenarios and their future complex developments, although profoundly different from the standard experimental and normative framework. In the conclusion, we recall the preceding efforts required by the policy itself to fully realize the promises of computational social sciences.

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The protection of the natural environment in armed conflicts and agent-based modelling

Qandeel

2023

Int. rev. Red Cross

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This article seeks to demonstrate the nexus between agent-related technology and the protection of the environment in armed conflicts, looking at how agent-based modelling and simulation (ABMS) can be used as a tool to protect the environment in armed conflicts. It further analyzes the precautionary principle and due regard, as relevant rules, and explains the legal benefits of deploying ABMS to protect and preserve the natural environment. The article argues that the deployment of ABMS helps States to better understand the environmental effects of conflicts, reassess their military activities and comply with the relevant applicable rules and norms.

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Agent-Based Modeling as a Legal Theory Tool

Cited by 4 publications

References 60 publications

Jurisprudence Meets Physics

Jurisprudence Meets Physics

Modelling Complexity with Unconventional Data: Foundational Issues in Computational Social Science

The protection of the natural environment in armed conflicts and agent-based modelling

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