Bruce Edmonds

Do you want your model findings to be used and have a place in academic or policy discourse? Then PAVE that place. For modelers, PAVE (Purpose, Assumptions, Validity, and Exploration) is a semi-structured method to communicate the suitability of models to inform a particular purpose. For users of model results, PAVE is a method for identifying the strength with which model findings inform a question of interest. For all audiences, PAVE distills more comprehensive model documentation protocols into a simple set of concepts for modelers and users alike to discuss. In doing so, PAVE solves a dilemma: there is an audience for whom formal protocols for documenting model development, testing, and analysis are everything. And there is an audience for whom such technical tools are a terrifying abyss, neither to be approached nor looked upon. PAVE is meant to help these audiences talk to each other about model findings.

Although models are essential for science and decision-making, their development and application are fragmented and therefore limited. Modeling standards can enhance uptake, interoperability, transparency and reuse of models, but are not widely used. We examined 49 modeling standards to determine how they are described, the principles they operationalize, the functions they support, and how they are applied across the modeling lifecycle. We identified interoperability as the most critical principle, as it encompasses communication between machines and people. The other identified principles aligned with the FAIR principles of findability, accessibility, and reusability. Transparency emerged as a concept that reinforced these elements. We developed an ontology for implementing the FAIR principles in modeling using standards. This ontology guides the selection of modeling standards for specific lifecycle stages, fosters coordination across modeling teams and disciplines, and provides a foundation for innovation, such as the development of composite standards to better serve interdisciplinary needs.

Humans tend to accept beliefs that are coherent with their other beliefs and reject those that do not fit with them. As a result of this, different people might arrive at different belief structures and the consequences of this on societal belief patterns are not known. In this chapter we show one way to induce possible ‘coherence structures’ for groups of people from survey data and then set them going, influencing each other, within a computer simulation of individual agents. Of course, people do not perfectly adhere to coherence strategy but are amenable to all sorts of other influences. In the simulation, these other influences are represented as randomness that affects whether to accept or reject beliefs. The simulation shows that the degree to which agents act randomly (rather than based on coherence) is critical to the outcomes. Low levels of randomness (i.e., strong strive for coherence) result in agents converging on more extreme beliefs. With high levels of randomness (i.e., weak striving for coherence) the simulated agents converge on moderate beliefs. It is with some intermediate adherence to coherence that the resulting spread of beliefs looks more like what we observe in the real world—spread out or polarised in some beliefs but converged in others. The approach is illustrated using data from the European Social Survey about attitudes in five dimensions.

Political polarization represents a rising issue in many countries, making it more and more important to understand its relation to cognitive‐motivational and social influence mechanisms. Yet, the link between micro‐level mechanisms and macro‐level phenomena remains unclear. We investigated the consequences of individuals striving for cognitive coherence in their belief systems on political polarization in society in an agent‐based model. In this, we formalized how cognitive coherence affects how individuals update their beliefs following social influence and self‐reflection processes. We derive agents' political beliefs as well as their subjective belief systems, defining what determines coherence for different individuals, from European Social Survey data via correlational class analysis. The simulation shows that agents polarize in their beliefs when they have a strong strive for cognitive coherence, and especially when they have structurally different belief systems. In a mathematical analysis, we not only explain the main findings but also underscore the necessity of simulations for understanding the complex dynamics of socially embedded phenomena such as political polarization.

Socio-Ecological System modelling projects are becoming increasingly complicated, with multiple actors and aspects being the norm. Such projects can cause problems for the modellers when this involves different elements, goals, philosophies, etc., all pulling in different directions – we call this “Chimaera Modelling.” Although such situations are common when you talk to modellers, they do not seem to be explicitly discussed in the literature. In this paper, we attempt to turn this perceived “inside” phenomenon into an “outside” phenomenon and to start a debate to increase transparency among the modelling community. We discuss the different aspects which may be relevant to this problem to start this debate, including: the underlying philosophy, modelling goals, extent of choice the modellers have, different stages of modelling, and kinds of actors that are involved. We further map out some of the dimensions with which Chimaera Modelling connects. We briefly discuss these and propose to the community as a whole to work on their methodological development, feasibility, risks and applicability as their resolution is far beyond the scope of this paper. We end with a brief description of the broad possible approaches to such situations. Our main message is a call for recognition of Chimaera Modelling as a likely side-effect of multi-stakeholder, multi-purpose projects, and to take this into account proactively at the project team level and be transparent about the tensions and contradictions that underly such modelling.