Analyses of so-called 'post-truth' discourse in populist politics have so far largely focussed on sorting it into cases of lying, bullshitting, bubble-like epistemic constraints, or alternative epistemic norms flouting objective truth. We review these proposals and point out problems with each. Some scholars, however, have recently drawn attention to how apparent assertions of facts in these contexts seem to be functionally entangled with expressing or affirming social identities. To get a clearer picture of what such an explanation might amount to, we differentiate four different ways in which social identities might be connected to apparently assertive discourse: signalling, expressive affirmation, dissonance reduction, and identity grounding. Distinguishing and deciding among these will matter not only for providing an accurate analysis of post-truth discourse, but also for determining the exact grounds on which it merits criticism and for what might be done about it.

Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. Moreover, we argue that philosophers contribute to computational modeling not only by building their own models, but also by thinking about the various applications of the method in philosophy and the sciences. In this context, we note that models in philosophy are usually simple, while models in the sciences are often more complex and empirically grounded. Bridging certain methodological gaps that arise from this discrepancy may prove to be challenging and fruitful for the further development of computational modeling in philosophy and beyond.

You have probably encountered the acronym ABM before, but in the unlikely event that you have not: Agent-based modeling (which is what ABM stands for) is a formal modeling technique in which complex (social) systems are represented on the level of individual agents and their mutual interactions, seeking to identify by means of simulation analysis how macro-level patterns emerge from agent interactions. In recent years, agent-based modelers (such as myself) have become eager and proud to procl...

Fear appeals constitute a frequent theme of populist rhetoric. One potential motive for this is that they decrease people's reliance on partisan habits and increase openness to new information. Political actors can use this effect to attract more ideologically distant groups of voters, but not without drawbacks. This paper analyses the strategic use of fear appeals in the framework of the Bounded-Confidence model. It is shown that attracting undecided voters between two opinion clusters is decisive for the success of a party's fear appeal strategy. Hence, fear appeals can increase a party's reach for new supporters, yet only if the party manages to clearly differentiate itself form ideological competitors.

This paper examines the claim that democratic decision making is epistemically valuable. Focussing on communication and voting, circumstances are identified under which groups are able to reliably identify the 'correct alternative.' Employing formal models from social epistemology, group performance under varying conditions in a simple epistemic task is scrutinized. Simulation results show that larger majority requirements can favour the veto power of closed-minded individuals, but can also increase precision in well-functioning groups. Reasonable scepticism against other people's opinions can provide a useful impediment to overly quick convergence onto a false consensus when independent information acquisition is possible.