Realistic models of decision-making and social interactions, considering the nature of memory and biases, continue to be an area of immense interest. Emotion and mood are a couple of key factors that play a major role in decisions, nature of social interactions, size of the social network, and the level of engagement. Most of the prior work in this direction focused on a single trait, behavior, or bias. However, this work builds an integrated model that considers multiple traits such as loneliness, the drive to interact, the memory, and mood biases in an agent. The agent system comprises of rational, manic, depressed, and bipolar agents. The system is modeled with an interconnected network, and the size of the personal network of each agent is based on its nature. We consider a game of iterated interactions where an agent cooperates based on its past experiences with the other agent. Through simulation, the effects of various biases and comparative performances of agent types is analyzed. Taking the performance of rational agents as the baseline, manic agents do much better, and depressed agents do much worse. The payoffs also exhibit an almost-linear relationship with the extent of mania.


Analyzing the Effects of Memory Biases and Mood Disorders on Social Performance

Social influence is an essential aspect of human social interaction. Influence propagation modeling has been extensively studied and applied in various research fields, such as the maximization of product adoption, the spread of infectious diseases, etc. The primary sources of information in any social network are the influential nodes that propagate this information further. Our study gives a method to identify the strong influencers in a network and dynamically predict the links/interactions made by them during information propagation. Previously, many efforts have been made to solve the link prediction problem in social networks such as the Adamic Adar index, based on the degree of common connecting neighbors; however, they disregard the fact that social networks can be dynamic over time—any network dataset collected by a snapshot at a particular instant of time may be incomplete and not reflect all the previous links formed. This may in turn result in false predictions. Our approach considers the current dynamics of the social network where information propagated by an agent leads to the prediction of new links with other nodes. Taking opinion propagation as an essential feature, we test our algorithm on Stanford’s Facebook network dataset and compare the results with the previous Adamic Adar index. The results obtained cover all the links predicted by Adamic Adar, as well as some new links formed dynamically. This is indicative that considering dynamic link formation is more efficient and helps address the problem of the incompleteness of network datasets reflecting the state at a time instant.

Dynamic Interactions by Strong Influencers in Social Networks using Opinion Propagation

Humans and AI / Human-Agent Interaction

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Shrisha Rao

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Presentations

Analyzing the Effects of Memory Biases and Mood Disorders on Social Performance

Dynamic Interactions by Strong Influencers in Social Networks using Opinion Propagation