Local Policing and Colorism
Methods and Tools for the Microsimulation and Forecasting of Household Expenditure
Dr. Mariam Kiran is a Research Scientist at LBNL, with roles at ESnet and Computational Research Division. Her current research focuses on deep reinforcement learning techniques and multi-agent applications to optimize control of system architectures such as HPC grids, high-speed networks and Cloud infrastructures.. Her work involves optimization of QoS, performance using parallelization algorithms and software engineering principles to solve complex data intensive problems such as large-scale complex decision-making. Over the years, she has been working with biologists, economists, social scientists, building tools and performing optimization of architectures for multiple problems in their domain.
I have been involved in agent-based modelling since the early nineties with a consistent attention to methdological improvement, institutional development and empirical issues. My mission is that ABM should be a routinely accepted research method (with a robust methodology) across the social sciences. To this end I have built diverse models and participated in research projects across economics, law, medicine, psychology, anthropology and sociology. I took a DPhil in economics on adaptive firm behaviour and then was involved in two research projects on money management and farmer decision making. Since 2006 I have worked at the Department of Sociology (now the School of Media, Communication and Sociology) at the University of Leicester. I was involved in the founding of JASSS and (more recently RofASSS https://rofasss.org) and have regularly served on the review panels for international conferences in the ABM community.
Decision making, research design and research methods, social networks, innovation diffusion, secondhand markets.
Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.