Reciprocity Is Not Governance: a short comment on the social-contract metaphor in peer review

A Comment on: Squazzoni, F. (2025) Editorial Note: We need to recognise that peer review is central to the ‘social contract’ of academic citizenship. Journal of Artificial Societies and Social Simu…

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A Reminder – computability limits on “vibe coding” ABMs (using LLMS to do the programming for us)

By Bruce Edmonds Introduction Machine-learning systems, including Large Language Models (LLMs), are algorithms trained on large datasets rather than something categorically different. Consequently,…

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Why Object-Oriented Programming is not the best method to implement Agent-Based Models

By Martin Hinsch Research Department of Genetics, Evolution and EnvironmentUniversity College London Introduction A considerable part of the history of software engineering consists of attempts to …

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Modelling Deep Structural Change in Agent-Based Social Simulation

By Thorid Wagenblast1, Nicholas Roxburgh2 and Alessandro Taberna3 1 Delft University of Technology, 0009-0003-5324-37782 The James Hutton Institute, 0000-0002-7821-18313 CMCC Foundation - Euro-Mediterranean Center on Climate Change, RFF-CMCC European Institute on Economics and the Environment, 0000-0002-0207-4148 Introduction Most agent-based models (ABMs) are designed around the assumption of a broadly stable system architecture. Whether exploring emergent dynamics or testing the effects of external interventions or stressors, such models typically operate with a fixed ontology – predefined agent types, attribute classes, behavioural repertoires, processes, and social and institutional structures.

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Make some noise! Why agent-based modelers should embrace the power of randomness

By Peter Steiglechner1, Marijn Keijzer2 1 Complexity Science Hub, Austria; steiglechner@csh.ac.at2 Institute for Advanced Study in Toulouse, France Abstract ‘Noisy’ behavior, belief updating, or decision-making is universally observed, yet typically treated superficially or not even accounted for at all by social simulation modelers. Here, we show how noise can affect model dynamics and outcomes, argue why injecting noise should become a central part of model analyses, and how it can help our understanding of (our mathematical models for) social behavior.

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Nigel Gilbert

By Corinna Elsenbroich & Petra Ahrweiler The first piece on winners of the European Social Simulation Association's Rosaria Conte Outstanding Contribution Award for Social Simulation. “Gilbert, a former sociologist of science, has been one of the chief links in Britain between computer scientists and sociologists of science” Nigel has always been and still is a sociologist - not only of science, but also of technology, innovation, methods and many other subfields of sociology with important contributions in theory, empirical research and sociological methods.

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Rosaria Conte (1952–2016)

By Mario Paolucci This is the “header piece” for a short series on those who have been awarded the “Rosaria Conte Outstanding Award for Social Simulation” awarded by the European Social Simulation …

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Short comment on ‘‘Editorial Note: We need to recognise that peer review is central to the ‘social contract’ of academic citizenship” (JASSS, 2025, 8, 1)

By Paola Galimberti The editorial note recently published in JASSS (Squazzoni 2025) focuses on the central role of peer review, an issue that has now become central to the debate on scholarly communication, research integrity and the role of artificial intelligence tools in research validation processes, particularly for journals owned by commercial publishers (e.g. Tennant et al. 2017). The fact that reciprocity between scientists is a core value of the academic system, in particular to ensure rigorous validation of scientific claims and self-correction mechanisms, is undeniable, and the call for a more responsible attitude on the part of scientists is therefore timely.

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Outlining some requirements for synthetic populations to initialise agent-based models

By Nick Roxburgh1, Rocco Paolillo2, Tatiana Filatova3, Clémentine Cottineau3, Mario Paolucci2 and Gary Polhill1 1 The James Hutton Institute, Aberdeen AB15 8QH, United Kingdom {nick.roxburgh,gary.polhill}@hutton.ac.uk 2 Institute for Research on Population and Social Policies, Rome, Italy {rocco.paolillo,mario.paolucci}@cnr.it 3 Delft University of Technology, Delft, The Netherlands {c.cottineau,t.filatova}@tudelft.nl Abstract. We propose a wish list of features that would greatly enhance population synthesis methods from the perspective of agent-based modelling.

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