mik_volkov["at"]hotmail.com
Volkov, Mikhail. Forthcoming. "A Morality Evolutionary Game Theory Can Model." Philosophy of Science.
Evolutionary game-theoretic (EGT) models of morality face powerful under-addressed objections. Critics claim the simulations fail to specify their explanandum, muddying their explanatory value. Additionally, morality is suggested to be not computationally representable, jeopardising the method’s general applicability. This paper explicates and addresses the objections. I argue at least one concrete explication of morality, emotionism coupled with functionally understood emotions, can be a plausible subject of EGT explanations. I demonstrate how fixing this explanandum assuages the methodological objections and provide a computational model as proof of concept. If successful, the contribution placates serious long-standing criticisms of EGT as a meta-ethical tool.
@article{volkov_2026_morality,
author = {Volkov, Mikhail},
title = {A Morality Evolutionary Game Theory Can Model},
journal = {Philosophy of Science},
year = {Forthcoming}}
Volkov, Mikhail. 2025. "The Root of Algocratic Illegitimacy." Philosophy & Technology 38 (2).
Would a political system where the governance was overseen by an algorithmic system be legitimate? The intuitive answer seems to be no. This paper considers the philosophical effort to justify this intuition that argue for algocracy, a rule by algorithms, being illegitimate. Taking as the paradigmatic example the anti-algocratic argument from Danaher that attempts to ground algocratic illegitimacy in the opacity of algocratic decision-making, it is argued that the argument oversimplfies the matters. Opacity can delegitimise—but not simpliciter. It delegitimises because of the presence of certain downstream violations of obligations and rights of the public that result from the opaque governance. Algocratic decision-makers, however, seem not to be subjects to these normative constraints in the relevant sense. The paper therefore argues that the standards of legitimacy that have been deployed for or against different kinds of human governance do not apply to the algorithmic decision-making systems with quite the same force. New avenues for rooting the illegitimacy of algocratic decision-makers have to be developed.
@article{volkov_2025_algocracy,
author = {Volkov, Mikhail},
title = {The Root of Algocratic Illegitimacy},
journal = {Philosophy \& Technology},
year = {2025},
volume = {38},
number = {2}}
[WIP] A paper on the conflict between prudential and epistemic values in individual decision-making on social media (Under review).
Epistemic environments shape their agents' intellectual virtues: I will end up with a different intellectual character after a year in a convent vs in an investment bank. Virtue epistemologists argue we should care about intellectual virtues, because they contribute to personal worth and epistemic achievement - then, we should worry about how epistemic environments influence their agents' intellectual character. Social media (SM) is arguably the largest epistemic environment today and the received view deems its effects on users' intellectual character to be negative. If SM is epistemically pernicious, we should try to intervene. This requires an understanding of the causes and motivations behind individual decision-making online. I propose an explanation for epistemically vicious patterns of SM usage in the form of a rational reconstruction, showing that such behaviour can be read as subjectively rational, given what typical users want from SM and what they know of its functioning. I utilise empirical evidence on online users' preferences and behaviour patterns, providing reasons to think this model is more than a just-so story and consider some immediate objections. If successful, this is not a just-so story consistent with our intuitions, but a how-actually explanation of why users engage in intellectually degrading behaviours.
[WIP] A paper on the ontology of complex social systems and ensuing unrealisability of proposals put forward by critics of agent-based models for improving the latter's predictive power.