Post-Doctoral Researcher in Economics at the University of Cape Town
Hello & welcome to my page. I am a postdoctoral research fellow at the African Institute of Financial Markets and Risk Management (AIFMRM), University of Cape Town. I have been based at AIFMRM since September 2016.
I completed my PhD in Economics in June 2015 at UNU-MERIT, School of Business and Economics, Maastricht University. Before that, I studied a B.SC in Mathematics and Physical Science, and a Master’s degree in Astronomy/Cosmology.
My primary research interests are in Game Theory (evolutionary game theory and models of social learning), Social Network Analysis and Economics of Science.
My PhD dissertation was titled ‘The evolution of beliefs and strategic behaviour’. While my PhD was mainly theoretical, I am currently combining my theoretical work with empirical studies of research collaboration networks.
I am in the job market for the academic year of 2018-2019.
Social networks provide a platform through which firms and governments can strategically diffuse products and desirable practices. This paper studies the process of strategic diffusion (i.e. diffusion by targeting specific individuals) in the presence of network externalities using an evolutionary game theory framework. We show that targeting is inefficient if the rate at which agents experiment on different choices is high, but below a certain threshold, targeting is economically reasonable. A firm/planner can reduce the cost of targeting by exploiting the process of contagion. A contagious choice requires a small set of initial adopters to trigger diffusion to the wider network. We show that the cost of making a product/practice contagious is lower for sparsely and uniformly connected networks than highly connected and/or less cohesive networks. We also show that the expected waiting time until a contagious choice is adopted by the entire network is independent of the population size. This implies that in large networks, even if the level of experimentation is very low, the diffusion process does not get trapped indefinitely in a suboptimal equilibrium.
Evolutionary models with persistent randomness employ stochastic stability as a solution concept to identify more reasonable outcomes in games with multiple equilibria. The complexity of computational methods used to identify stochastically stable outcomes and their lack of robustness to the interaction structure limit the applicability of evolutionary selection theories. This paper identifies p-dominance and contagion threshold as the properties of strategies and interaction structure respectively that robustly determine stochastically stable outcomes. Specifically, we show that p-dominant strategies, which are best responses to any distribution that assigns them a weight of at least p, are stochastically stable in networks with contagion threshold of at least p.
This paper develops a framework for word-of-mouth learning in networks where agents strategically decide when to take an irreversible action. Agents face a trade-off between taking an irreversible action early enough to avoid the cost of waiting and waiting to receive more information to increase confidence in their choice. We characterize equilibrium exit times and establish conditions for correct learning in large societies. The necessary conditions for correct learning are: (i) no single or small group of agents should have unbounded influence as measured by conditional in-degree; (ii) The underlying network must have a bounded diameter. Finally, we show that the presence of noise in signals prolongs exit times, and hence increases the likelihood of asymptotic learning.
Centralized network structures, which consist of at least one actor – a central agent – whose behaviour is observed by the rest of the group are predominant across social, political and economic settings. In public and corporate sectors, central agents such as managers and CEOs, shape organizational culture and identity, which in turn affects organizational performance. This paper studies how central agents affect behaviour formation through social learning. We show that although central agents play a crucial role in driving the group to a consensus, they do not necessarily exert the most influence on the group’s equilibrium behaviour. In equilibrium, an agent’s influence corresponds to her eigenvector centrality. We also examine the convergence rate of behaviour formation and show that it depends on the degree to which central agents facilitate group cohesion.
This paper studies how individual prejudice – a set of preconceived and inflexible opinions – and group cohesion generate everlasting public disagreement in models of learning by averaging. We consider an endogenous model of opinion formation where agents compromise between respecting their own personal prejudice and conforming their opinions to those held by others with whom they share close ties. We quantify the extent of equilibrium disagreement and show that its magnitude increases with the intensities of prejudice and group cohesion. We propose a new measure of the intensity of group cohesion that depends on the frequency of interactions between cohesive subgroups. We also examine the speed of learning in the proposed model and show that it decreases logarithmically with the intensity of prejudice but increases with the intensity of group cohesion.
When preparing a research article, academics engage in informal intellectual collaboration by asking their colleagues for feedback. This collaboration gives rise to a social network between academics. We study whether informal intellectual collaboration with an academic who is more central in this social network results in a research article having higher scientific impact. To address the well-known reflection problem in estimating network effects, we use the assignment of discussants at NBER summer institutes as a quasi-natural experiment. We show that manuscripts discussed by a discussant with a 10% higher than average Bonacich centrality rank results in 1.4% more citations and a 5% higher probability that an article is published in a top journal. To illustrate our results, we develop a structural model in which a positive externality from intellectual collaboration implies that collaborating with a more central colleague results in larger scientific impact of the research article. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2877586
This study is the first to examine the role of discussants in academic knowledge production. Comparing articles of similar quality with and without discussants, we find that having a discussant increases a paper’s probability of publication in prestigious journals, but not its citation count. Conditional on having a discussant, citation count and probability of publication in a prestigious journal increase in the discussants’ prolificness. This supports the existence of a quality channel through which discussants improve the inherent quality of a paper. Conversely, we do not find evidence for the existence of a diffusion channel whereby papers garner more citations because discussants diffuse information about the paper within their social network.
This paper studies evolutionary game dynamics with local interactions. We consider a ring interaction structure where each agent interacts with a subset of k other agents, k/2 to the left and right. We show that in such a structure, iterated 1/k-best response sets are stochastically stable. A subset of strategies forms a 1/k-best response set if strategies in the set are best responses to any distribution that assigns them a total weight of at least 1/k. Iterated application of this idea leads to the concept of iterated 1/k-best response sets. We also demonstrate the effect of connectivity (density of the interaction structure) and find that iterated p-best response sets, for 1/k<p<1/2, are stochastically stable provided the k is sufficiently smaller than the population size. Above a threshold level of connectivity however, iterated p-best response sets need not be stochastically stable.
Timing is an important component of many economic and investment decisions. When faced with uncertainty regarding the rewards from available options, agents may seek information from their peers to inform their decisions. This paper develops a framework of information exchange through social learning. Although waiting to receive information from peers improves the accuracy of decision, there may be material losses associated with waiting. Agents strategies thus involve choosing an optimal time to stop waiting for information and take an action (e.g. make an investment). We examine how the architecture of the underlying interaction structure influences stopping times. We find that neighbourhood centrality, a network measure of the number of an agent’s neighbours within a given radius, determines stopping times. Specifically, centrally placed agents (with higher neighbourhood centrality) have shorter stopping times, while peripheral (in the network) agents have longer stopping times. This framework helps explain empirical observation regarding correlations in investment decisions within and not across regions.
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