Social Epistemology and Validation in Agent-Based Social Simulation

Anzola, David

doi:10.1007/s13347-021-00461-8

Cited by 5 publications

(4 citation statements)

References 93 publications

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“…Any computer simulation, for example, faces problems of trustworthiness linked to epistemic opacity of the computation (Durán & Formanek 2018). At the same time, practitioners of agent-based social simulation face heightened risks with the intelligibility, transparency, and commensurability of representation, given the unformalised and multiparadigmatic nature of social theory and the syntactic and semantic flexibility of computer languages (Anzola 2021b;Poile & Safayeni 2016).…”

Section: 8mentioning

confidence: 99%

The Ethics of Agent-Based Social Simulation

Anzola

Barbrook-Johnson

Gilbert

2022

JASSS

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The academic study and the applied use of agent-based modelling of social processes has matured considerably over the last thirty years. The time is now right to engage seriously with the ethics and responsible practice of agent-based social simulation. In this paper, we first outline the many reasons why it is appropriate to explore an ethics of agent-based modelling and how ethical issues arise in its practice and organisation. We go on to discuss different approaches to standardisation as a way of supporting responsible practice. Some of the main conclusions are organised as provisions in a draft code of ethics. We intend for this draft to be further developed by the community before being adopted by individuals and groups within the field informally or formally.

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Section: 8mentioning

confidence: 99%

The Ethics of Agent-Based Social Simulation

Anzola

Barbrook-Johnson

Gilbert

2022

JASSS

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“…These criteria depend on several representational issues regarding the objects being represented, their properties and the way these properties are measured. This is a crucial element for agent-based modeling in social science, since results are not analytically derived and there could be multiple (non-equivalent) implementations of the same phenomenon (Anzola 2021c). Because there is not always an output that is readily available for contrast with data produced by previous studies and models, a "domain of validity" (Axtell et al 1996) must be established prior to the implementation of verification techniques, such as using known parameter values.…”

Section: Verification and Representationmentioning

confidence: 99%

“…This longstanding view in equation-based modeling does not fit the practice of agent-based modeling. Given the semantic and syntactic flexibility of social theory and programming languages, agent-based models are often articulated using unique approaches and reconstructions of the theories of properties and measurement in the social domain (Anzola 2021c). As mentioned, material, cognitive and social resources influence the different stages of the simulation life cycle in such a way so as to make the model itself the object of inquiry.…”

Section: Renouncing the Dual Evaluation Schemementioning

confidence: 99%

The Theory-Practice Gap in the Evaluation of Agent-Based Social Simulations

Anzola

2021

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Argument Agent-based social simulations have historically been evaluated using two criteria: verification and validation. This article questions the adequacy of this dual evaluation scheme. It claims that the scheme does not conform to everyday practices of evaluation, and has, over time, fostered a theory-practice gap in the assessment of social simulations. This gap originates because the dual evaluation scheme, inherited from computer science and software engineering, on one hand, overemphasizes the technical and formal aspects of the implementation process and, on the other hand, misrepresents the connection between the conceptual and the computational model. The mismatch between evaluation theory and practice, it is suggested, might be overcome if practitioners of agent-based social simulation adopt a single criterion evaluation scheme in which: i) the technical/formal issues of the implementation process are tackled as a matter of debugging or instrument calibration, and ii) the epistemological issues surrounding the connection between conceptual and computational models are addressed as a matter of validation.

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“…We understand that social simulation models are formal entities created by researchers as they please and, as such, they belong to the universe of formal systems, not to the natural world(Rosen 2012, p. 45). There are, however, alternative ways of understanding the role of computer simulations in science (see e.g Winsberg 2009;Barberousse & Vorms 2014;Parker 2017;Anzola 2021b;Alvarado 2022Popper (2005) actually uses 'logical' instead of 'formal'.…”

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confidence: 99%

Social Simulation Models as Refuting Machines

Mauhé

Izquierdo

2023

JASSS

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This paper discusses a prominent way in which social simulations can contribute (and have contributed) to the advance of science; namely, by refuting some of our incorrect beliefs about how the real world works. More precisely, social simulations can produce counter-examples that reveal something is wrong in a prevailing scientific assumption. Indeed, here we argue that this is a role that many well-known social simulation models have played, and it may be one of the main reasons why such well-known models have become so popular. To test this hypothesis, here we examine several popular models in the social simulation literature and we find that all these models are most naturally interpreted as providers of compelling and reproducible (computer-generated) evidence that refuted some assumption or belief in a prevailing theory. By refuting prevailing theories, these models have greatly advanced science and, in some cases, have even opened a new field of research.

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Social Epistemology and Validation in Agent-Based Social Simulation

Cited by 5 publications

References 93 publications

The Ethics of Agent-Based Social Simulation

The Ethics of Agent-Based Social Simulation

The Theory-Practice Gap in the Evaluation of Agent-Based Social Simulations

Social Simulation Models as Refuting Machines

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