Alperen "Alp" Yasar

I am a Ph.D. candidate in a joint degree program between the Economics Department of Ca' Foscari University of Venice and the Applied Mathematics Department of Paris I Pantheon-Sorbonne University.

I am interested in complex social phenomena that arise from simple human behaviors. I create analytical models that predict biases or fallacies via aggregation functions and/or games, then I use computational tools to study the emergent behavior in the community.

My doctoral education is funded thanks to the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956107, "Economic Policy in Complex Environments (EPOC)".

Summary

During my dissertation, I studied mental models from different perspectives.

The emergence of discrimination due to miscategorization

This code is used for the following paper:

Abstract:

This study explores the emergence of discrimination based on observable characteristics. In many instances, agents presume differences arising from traits such as race or gender, even when these parameters are irrelevant to the situation at hand. This paper intends to reveal an emergent behavior and a persistent culture of discrimination caused by miscategorization in strategic interactions. We assume that agents occasionally engage in conflicts modeled as asymmetric hawk and dove games, where boundedly rational agents may categorize their opponents based on observable traits to make effective decisions. Three categorization strategies are considered: fine-grained, regular, and coarse-grained. Subsequently, an evolutionary agent-based model is employed to examine the performance of these strategies in a dynamic environment. The results demonstrate that fine-grained categorization provides an advantage when the cost of fighting is low, while coarse-grained categorizers exhibit more peaceful behavior, gaining an advantage when the cost of conflict is high. Our primary finding indicates the emergence of discrimination based on non-relevant traits, manifested through consistent aggressive behavior towards individuals possessing these traits.

Published in:

  • Yasar, Alperen. 2024. "The emergence of discrimination due to miscategorization.” International Journal of Organization Theory & Behavior. DOI.

Frequentist belief update under ambiguous evidence in social networks

Abstract:

In this paper, we study a frequentist approach to belief updating in the framework of Dempster-Shafer Theory (DST). We propose several mechanisms that allow the gathering of possibly ambiguous pieces of evidence over time to obtain a belief mass assignment. We then use our approach to study the impact of ambiguous evidence on the belief distribution of agents in social networks. We illustrate our approach by taking three representative situations. In the first one, we suppose that there is an unknown state of nature, and agents form belief in the set of possible states. Nature constantly sends a signal which reflects the true state with some probability but which can also be ambiguous. In the second situation, there is no ground truth, and agents are against or in favor of some ethical or societal issues. In the third situation, there is no ground state either, but agents have opinions on left, center, and right political parties. We show that our approach can model various phenomena often observed in social networks, such as polarization or bounded confidence effects.

Highlights:

  • Creates a frequentist belief update framework for Dempster-Shafer Theory, allowing us a subjective belief system.
  • Proposes several update rules for frequentist update methodology and studies their properties.
  • Shows that DST allows for a flexible belief update mechanism in social networks.
  • Studies various settings, including ambiguous evidence, network polarization, and echo chambers under DST.

Published in:

  • Grabisch, M., & Yasar, M. A. (2024). Frequentist Belief Update Under Ambiguous Evidence in Social Networks. International Journal of Approximate Reasoning, 172, 109240. DOI.

Vocabulary Aggregation

Abstract:

A vocabulary is a list of words designating subsets of points from a grand set X. We model a vocabulary as a partition of X and study the aggregation of individual vocabularies into a collective one. We characterize aggregation rules when X is linearly ordered and each individual partition is formed by order intervals. Notably, we allow for individual vocabularies to differ both in the number and in the extension of their words.

Working paper:

  • LiCalzi, M., & Yasar, M. A. (2024). Vocabulary aggregation. Available in SSRN.

CV

Education

  • PhD Student in Applied Mathematics in Paris I Pantheon-Sorbonne University (2021 - Current)
  • PhD Student in Economics in Ca' Foscari University of Venice (2021 - Current)
  • Galatasaray University: Master's in Economics (2018-2020)

Publications

  • Grabisch, M., & Yasar, M. A. (2024). Frequentist Belief Update Under Ambiguous Evidence in Social Networks. International Journal of Approximate Reasoning, 172, 109240. DOI.
  • Yasar, Alperen. 2024. "The emergence of discrimination due to miscategorization.” International Journal of Organization Theory & Behavior. DOI.
  • LiCalzi, M., & Yasar, M. A. (2024). Vocabulary aggregation. Available in SSRN.

Conference presentations

The emergence of discrimination in power struggles:

  • AMASES XLVI - Annual Meeting of the Italian Association for Mathematics Applied to Social and Economic Sciences; Palermo, Italy; 22.9 – 24.9.2022;
  • 17th BiGSEM Doctoral Workshop on Economics and Management; Bielefeld, Germany; 12.12. - 13.12.2022;
  • 26th Annual Workshop on Economic Science with Heterogeneous Interacting Agents; Koper, Slovenia; 22.06 - 24.06-2023.

Frequentist belief update under ambiguous evidence in social networks:

  • 56th Annual Meeting of the Society for Mathematical Psychology; Amsterdam, Netherlands; 18.07 - 21.07.2023;
  • AMASES XLVII - Annual Meeting of the Italian Association for Mathematics Applied to Social and Economic Sciences; Milan, Italy; 20.9 - 22.9.2023.

Grants

  • Marie Skłodowska-Curie Fellowship: My doctoral education has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956107, see EPOC for more information.

Teaching

  • Mathematics for social sciences, 2023-2024 Fall.