Essays on using information systems for social progress: Agent-based models for a better world

Poverty and inequality continue to be a cause of suffering in global scale, hampering inclusive growth. In this dissertation, we present two essays, which utilises agent-based modelling, to examine two different methods for alleviation of poverty and inequality, namely giving and lending. First essay builds a simple economy model in which only transaction is money transfer between agents, in a macro level approach. It simulates compulsory giving, taxation, and voluntary giving, almsgiving (zakat), both separately and simultaneously, to measure their effectiveness in inequality reduction. Results of experiments with different rates and recipient groups demonstrate that they are both effective at decreasing nonuniformity in wealth distribution, and can substitute each other. Giving a portion of wealth (zakat) reduces inequality in random selection of recipients as well. In specific conditions, if share of wealth given as alms exceeds some critical values, inequality is observed to raise again. Second essay focuses on lending with a micro level approach and models automated relending mechanism in peer-to-peer (P2P) microlending platforms. It aims to find the selection criteria of projects to loan, that enables relatively better resource utilisation. Results show that in conditions of loan default or full funding of projects, criterion of previous experience of entrepreneurs provides better effectiveness in resource utilisation, whereas in equal but partial funding of projects with no loan default, criteria of less additional fund needed or shorter time to expiration of the campaign achieve better utilisation.