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Ronja Hotz

Publications and source records attributed to Ronja Hotz.

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Empirical behavioural heterogeneity shapes the dynamics of an agent-based land use model

Land use models often represent decision-makers as homogeneous and rational, overlooking socio-psychological diversity and potentially generating rapid, coordinated land use responses that contrast with observed land use patterns. Here, we integrate an empirical typology of European forestry practitioners into an agent-based land use model by translating survey-based behavioural profiles into cognitive parameters governing endogenous decision-making processes. We distinguish five practitioner types and parametrise heterogeneous agent populations according to the empirically observed distribution of these types. Using a stylised model landscape, we compare these heterogeneous populations against a homogeneous rational-choice baseline and single-type populations. Compared with the homogeneous rational-choice baseline, heterogeneous populations dampen synchronised responses to changing ecosystem service demand, producing more gradual land use dynamics and a higher prevalence of medium intensity management, that better reflect empirical land use patterns. Our experiments reveal that similar land use patterns can emerge through different behavioural mechanisms, while the behaviour of individual decision-making types depends strongly on the population context in which they are embedded. Together, these two findings show that individual behavioural responses and land use outcomes co-evolve rather than decision-making types mapping onto fixed management practices. Explicitly representing socio-psychological diversity can therefore improve the realism and policy-relevance of agent-based land-use models by capturing feedbacks between cognition, social interactions, and emergent land use dynamics. Empirical data may make this representation possible, but models can also use this capability to explore behavioural heterogeneity as a key source of uncertainty when data are absent.

cs.CE

Modelling Socio-Psychological Drivers of Land Management Intensity

Land management intensity shapes ecosystem service provision, socio-ecological resilience and is central to sustainable transformation. Yet most land use models emphasise economic and biophysical drivers, while socio-psychological factors influencing land managers' decisions remain underrepresented despite increasing evidence that they shape land management choices. To address this gap, we develop a generic behavioural extension for agent-based land use models, guided by the Theory of Planned Behaviour as an overarching conceptual framework. The extension integrates environmental attitudes, descriptive social norms and behavioural inertia into land managers' decisions on land management intensity. To demonstrate applicability, the extension is coupled to an existing land use modelling framework and explored in stylised settings to isolate behavioural mechanisms. Results show that socio-psychological drivers can significantly alter land management intensity shares, landscape configuration, and ecosystem service provision. Nonlinear feedbacks between these drivers, spatial resource heterogeneity, and ecosystem service demand lead to emergent dynamics that are sometimes counter-intuitive and can diverge from the agent-level decision rules. Increasing the influence of social norms generates spatial clustering and higher landscape connectivity, while feedbacks between behavioural factors can lead to path dependence, lock-in effects, and the emergence of multiple stable regimes with sharp transitions. The proposed framework demonstrates how even low levels of behavioural diversity and social interactions can reshape system-level land use outcomes and provides a reusable modelling component for incorporating socio-psychological processes into land use simulations. The approach can be integrated into other agent-based land use models and parameterised empirically in future work.

cs.CE

Simulating multiple human perspectives in socio-ecological systems using large language models

Understanding socio-ecological systems requires insights from diverse stakeholder perspectives, which are often hard to access. To enable alternative, simulation-based exploration of different stakeholder perspectives, we develop the HoPeS (Human-Oriented Perspective Shifting) modelling framework. HoPeS employs agents powered by large language models (LLMs) to represent various stakeholders; users can step into the agent roles to experience perspectival differences. A simulation protocol serves as a "scaffold" to streamline multiple perspective-taking simulations, supporting users in reflecting on, transitioning between, and integrating across perspectives. A prototype system is developed to demonstrate HoPeS in the context of institutional dynamics and land use change, enabling both narrative-driven and numerical experiments. In an illustrative experiment, a user successively adopts the perspectives of a system observer and a researcher - a role that analyses data from the embedded land use model to inform evidence-based decision-making for other LLM agents representing various institutions. Despite the user's effort to recommend technically sound policies, discrepancies persist between the policy recommendation and implementation due to stakeholders' competing advocacies, mirroring real-world misalignment between researcher and policymaker perspectives. The user's reflection highlights the subjective feelings of frustration and disappointment as a researcher, especially due to the challenge of maintaining political neutrality while attempting to gain political influence. Despite this, the user exhibits high motivation to experiment with alternative narrative framing strategies, suggesting the system's potential in exploring different perspectives. Further system and protocol refinement are likely to enable new forms of interdisciplinary collaboration in socio-ecological simulations.

cs.AI

Coarse-graining master equation for periodically driven systems

We analyze Lindblad-Gorini-Kossakowski-Sudarshan-type generators for selected periodically driven open quantum systems. All these generators can be obtained by temporal coarse-graining procedures, and we compare different coarse-graining schemes. Similar as for undriven systems, we find that a dynamically adapted coarse-graining time, effectively yielding non-Markovian dynamics by interpolating through a series of different but invididually Markovian solutions, gives the best results among the different coarse-graining schemes, albeit at highest computational cost.

quant-ph