Iza Romanowska

Comparative analyses of diversity in human populations often rely on historical, archival and observational data, which are systematically shaped by uneven documentation, preservation and research attention. Such collection bias can distort comparisons across populations, regions or time periods, obscuring genuine patterns of variation or generating spurious ones. Standard approaches that equalize sample size or sampling effort implicitly assume comparable sampling completeness, which is an assumption rarely satisfied in human and historical datasets. Here, we evaluate coverage-based standardization, adapted from ecological diversity estimation, as a general framework for comparative diversity analysis under biased and incomplete observation. Using population-level simulations, we show that coverage-based approaches reliably recover true diversity relationships across multiple, qualitatively distinct mechanisms of collection bias, whereas sample-size-based methods yield systematically distorted inferences. We illustrate the utility of this framework with an application to a large historical cultural dataset, where correcting for uneven documentation substantially refines inferred diversity patterns. By conditioning comparisons on sampling completeness rather than raw sample size, coverage-based standardization offers a principled and broadly applicable solution for comparative research in any domain where observation is biased, incomplete or historically contingent.

SHEEP (Simulating Hominin Expansion in the Early Pleistocene) is an Agent-based model (ABM) of the first Out of Africa hominin dispersal, designed to explore the spatial pattern of Lower Palaeolithic site distribution in Europe. The SHEEP model uses a deterministic environmental approach to evaluate potential routes into Europe and their impact on the pattern of site distribution in the eastern and western parts of the continent. The model consists of three main simulated elements: 1) population growth, 2) a spatial spreading process and 3) the friction map, which includes both environmental and topographic information. The impact of different geographical projections on the outcome of the model has been evaluated. The results show that we are unable to reproduce the null hypothesis and observed spatial pattern of European sites is not a result of differences in dispersal routes. This case study showcases the use of ABM as a simple and efficient tool for hypotheses testing with a high potential for archaeological applications.

Departing from tax-funded UBI approaches this paper proposes a Personal Dividend that socialises the money-creation power hitherto monopolised by private banks. Central to the proposal is a voluntary migration of funds to the digital Monetary Commons, with increased personal dividends stabilising money supply through the negative money multiplier, thus reducing inflationary pressures, eliminating the need for Quantitative Easing, enabling revenue-neutral Pigouvian taxation, reducing wealth inequality, and enhancing systemic stability. Using agent-based simulation, we test adoption feasibility under various scenarios. The scheme consistently succeeds across diverse conditions, demonstrating its potential as a sustainable and voluntary alternative to conventional UBI frameworks.

Resilience—the ability of socio-ecological systems to withstand and recover from shocks—is a key research and policy focus. Definitions of resilience differ between disciplines, however, and the term remains inadequately operationalized. Resilience is the outcome of variable behavioral decisions, yet the process itself and the strategies behind it have rarely been addressed quantitatively. We present an agent-based model integrating four common risk management strategies, observed in past and present societies. Model outcomes under different environmental regimes, and in relation to key case studies, provide a mapping between the efficacy (success in harm prevention) and efficiency (cost of harm prevention) of different behavioral strategies. This formalization unravels the historical contingency of dynamic socio-natural processes in the context of crises. In discriminating between successful and failed risk management strategies deployed in the past—the emergent outcome of which is resilience—we are better placed to understand and to some degree predict their utility in the contemporary world.

Agent-based modelling (ABM) is one of the most widely applied computational modelling research techniques in archaeology, because it is well suited to the questions asked by archaeologists. However, the access to vocational training for ABM is limited. In this paper we present the Open Educational Resources (OER) that we produced in an international cooperation. For this purpose, we created online and open tutorials for ABM for archaeologists. In addition, how-to guides and support material were created, both for teachers and students. The materials adhere to international (European) standards in relation to education and the Digital Skills Passport for archaeology. The teaching materials have been well tested using a diverse group of archaeologists during various international conferences and meetings. It was shown that the knowledge and skills of the participants in relation to ABM improved. In addition, the demand for the teaching materials and the workshops was high, ensuring that the tutorials on ABM will be used in the future.

This study investigates the impact of the reflective-impulsive dimension of cognitive styles on recollection and the accuracy of a criminal events. In a VR-based experimental study, 150 participants were exposed to both criminal and neutral scenarios and subsequently asked to report everything they remembered about them. Cognitive styles were assessed using a computer-administered Matching Familiar Figures Test. The findings highlight that individuals with a reflective cognitive style consistently provided detailed descriptions of the events, irrespective of the context of encoding. In contrast, individuals with an impulsive cognitive style displayed context-specific effects, notably reduced performance when recalling emotionally charged criminal events. Notably, cognitive style did not moderate the accuracy of testimony. Given that the subjects who observed the crime achieved a lower overall accuracy rate than those who observed the neutral video, it appears that accuracy is directly affected by the criminal content.

Specification and Description Language (SDL) is a language that can represent the behavior and structure of a model completely and unambiguously. It allows the creation of frameworks that can run a model without the need to code it in a specific programming language. This automatic process simplifies the key phases of model building: validation and verification. SDLPS is a simulator that enables the definition and execution of models using SDL. In this paper, we present a new library that enables the execution of SDL models defined on SDLPS infrastructure on a HPC platform, such as a supercomputer, thus significantly speeding up simulation runtime. Moreover, we apply the SDL language to a social science use case, thus opening a new avenue for facilitating the use of HPC power to new groups of users. The tools presented here have the potential to increase the robustness of modeling software by improving the documentation, verification, and validation of the models.