Trust and mistrust in a networked society [electronic resource]

Internet based socio-economic interactions often involve strangers, and as a result, are shrouded in varying degrees of mutual mistrust. Effectively combating this mistrust is critical to enabling trusted interactions on the Internet. This dissertation addresses the question of how to foster trust in Internet based socio-economic interactions in two contexts: preventing misbehavior such as spam, fraud, and free-riding in content-sharing systems (e.g., BitTorrent, Yelp, YouTube, email) and online marketplaces, and countering the apparent polarization in society through online social systems that influence the way people form opinions. Most online content sharing systems and marketplaces use centralized virtual currencies or reputation systems to prevent misbehavior and foster trust. We study an alternate model of decentralized currency called credit networks, that is based on trust between individuals, and hence is robust against the problem of cheap pseudonyms. Individuals in a credit network pay for goods and services by issuing their own IOUs (obligations), and express trust in terms of the number of IOUs they are committed to accept from each other. We analyze the liquidity---the ability to trade---in credit networks in terms of the long-term transaction failure probability when individuals repeatedly transact under a probabilistic transaction regime. We show analytically and using simulations that, surprisingly, liquidity in credit networks having a number of well-known network topologies is nearly identical to that in centralized currency systems. We also analyze the structural and economic properties of the credit networks that are formed when individuals strategically decide how much to trust each other. Our results help establish credit networks as a viable alternative to centralized reputation and virtual currency systems. While the Internet has helped bring us closer by making it easier to communicate, there is considerable anecdotal and empirical evidence that we are getting more polarized as a society. In particular, empirical studies have shown that homophily, i.e., greater interaction between like-minded individuals, results in polarization. We study polarization through a mathematical model of opinion formation in a social network. We adopt the view that polarization is not a property of a state of society; instead it is a property of the dynamics through which people form opinions. We say that opinion formation dynamics are polarizing if they result in an increased divergence of opinions. We show that DeGroot's well-known model of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize DeGroot's model to account for a phenomenon well-known in social psychology as confirmation bias: When presented with mixed or inconclusive evidence on a complex issue, individuals draw undue support for their initial position, thereby arriving at a more extreme opinion. We show that in a simple model of homophilous networks, our biased opinion formation process results in polarization if individuals are sufficiently biased. In other words, homophily alone, without confirmation bias, is not sufficient to polarize society. We discuss the implications of our analysis for the design of online social systems that influence the way people form opinions. In particular, we use confirmation bias as a framework to analyze the polarizing effect of Internet based recommender systems that deliver content tailored to our specific tastes. We also describe an online social platform called Widescope that we built to foster dialogue and enable consensus on government budgets and budget deficits. We report the results of two human subject experiments in which each subject created a federal budget on Widescope and then the subjects interacted in pairs to arrive at a greater consensus on the budget.

Keywords: Management Science and Engineering.
Advisor: Goel Ashish primary advisor. Thesis advisor
Johari Ramesh
Saberi Amin advisor. Thesis advisor