This thesis intends to utilize mathematical models for testing the development of sustainable urban neighbourhoods and analyze the impact of these developments at city level using dynamic and multiobjective optimization techniques. These techniques aim to monitor and lower urban carbon emission levels, while predicting the municipality’s projected tax revenues. This study shows how multiple decision making models can operate and re- late to help analyze the implementation of a sustainable neighbourhood design in a mid-size urban area.

This dissertation consists of three essays examining the impact of information asymmetries in context of entrepreneurship and finance. Chapter 2, coauthored with Silvana Krasteva and Liad Wagman, focuses on the asymmetries between a firm and its (researcher) employee and studies the problem faced by a (researcher) employee when choosing whether to pursue an innovative idea as part of his employment at a firm or to form a start-up. An idea by its stand-alone value and by the degree of (positive or negative) externality that it may impose on the employing firm’s existing profits if brought to market. The employee has private information about the innovation and his ability to independently develop it. Internal exploration, while allowing the employee to take advantage of any exploration support offered by the firm, reduces the employee’s claim over his idea. We find that external exploration takes place for ideas weakly related to the firm’s existing offerings, with other ideas being explored internally. We show that if the firm increases its support for exploration, it can induce the internal research of a wider range of ideas; however, by doing so, the firm also increases the likelihood of employees departing to pursue independent ventures at a later stage of development. Chapter 3 analyzes the benefits of reducing information asymmetry in the credit markets. In their attempt to make more informed decisions, lenders of- ten use a variety of information contained in a borrower ’s credit report. We find that if a borrower expects his future lenders to base their decisions not only on his repayment history but also on other factors like his income, length of history, etc., then his incentives to repay his present loan are weakened. In this case, he is more likely to strategically default on his loan especially for very high levels of interest rates. However, use of this extra information assists the lender in expeditious screening of the borrowers. Based on our results, we recommend that, in order to minimize defaults, more repayment history based products should be offered by the lenders. Evidence supporting the validity of this recommendation is provided in Chapter 4, coauthored with Vijetha Koppa. Using data from Prosper.com, we analyze the effect of reporting repayment histories to an additional credit bureau on borrowers’ default rates and lenders’ internal rates of return. A differences- in-differences comparison between high risk and low risk borrowers reveals that for high risk borrowers, the default rates were 9 to 11 percentage points greater and the internal rates of return were 13 percentage points lower in the pre-change period.

The goal of this thesis is to explore the use of reinforcement learning (RL) in commercial computer games. Although RL has been applied with success to many types of board games and non-game simulated environments, there has been little work in applying RL to the most popular genres of games: first-person shooters, role-playing games, and real-time strategies. In this thesis we use a first-person shooter environment to create computer players, or bots, that learn to play the game using reinforcement learning techniques. / We have created three experimental bots: ChaserBot, ItemBot and HybridBot. The two first bots each focus on a different aspect of the first-person shooter genre, and learn using basic RL. ChaserBot learns to chase down and shoot an enemy player. ItemBot, on the other hand, learns how to pick up the items---weapons, ammunition, armor---that are available, scattered on the ground, for the players to improve their arsenal. Both of these bots become reasonably proficient at their assigned task. Our goal for the third bot, HybridBot, was to create a bot that both chases and shoots an enemy player and goes after the items in the environment. Unlike the two previous bots which only have primitive actions available (strafing right or left, moving forward or backward, etc.), HybridBot uses options. At any state, it may choose either the player chasing option or the item gathering option. These options' internal policies are determined by the data learned by ChaserBot and ItemBot. HybridBot uses reinforcement learning to learn which option to pick at a given state. / Each bot learns to perform its given tasks. We compare the three bots' ability to gather items, and ChaserBot's and HybridBot's ability to chase their opponent. HybridBot's results are of particular interest as it outperforms ItemBot at picking up items by a large amount. However, none of our experiments yielded bots that are competitive with human players. We discuss the reasons for this and suggest improvements for future work that could lead to competitive reinforcement learning bots.

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This thesis examines how young children experience games and how that question is relatedto gender. The examination of this question is conducted through interviews with a group of24 Swedish fifth-grade pupils. The paper also draws from theories and concepts found inestablished literature on gender and games. The results of this thesis informs the theory ofviolence as a masculine preference as well as the separation of gender identities from biologicalsexes.

The video game industry is one of the fastest growing sectors in the U.S. economy, with violent video games topping the charts among fan favorites. Due to frequent violence and mass shootings that have struck our nation in recent years, the gaming industry has been subject to much blame for endorsing violent acts. While many researches and psychologists have conducted experiments and meta-analytic reviews to test the correlation between violent video games and mass shootings, the debate remains prevalent in the media, to date. This thesis will not aim to prove whether or not violent video games are largely responsible for malicious attacks, but rather analyze why the blame is often adverted specifically to the video game industry in the aftermath of public massacres. This paper will analyze the positive and negative effects of gaming, the social and behavioral tendencies often developed from playing video games, and the current controversies surrounding the industry.

In social theory, games are frequently presented as archetypal examples of activities governed by formal rules. Drawing upon ethnomethodology and figurational sociology, this project provides an analysis of the constituting of games and identifies the inadequacies of this conventional formalist wisdom. Applying and elaborating upon Garfinkel's work, two case-studies are presented which are designed to display the other dimensions of rule-following through which players accomplish a viable game. Analysis also reveals that this collaborative work does not preclude differing interpretations of the rules of the game. Changes to the rules are invoked in an attempt to remove their fringe of incompleteness in governing game-conduct and in the interests of creating and sustaining a viable game. These case-studies and a typification of game-rules provides the basis for analysis of the constituting of institutionalised 'invasion' games such as basketball, rugby union, soccer, rugby league, and netball. Interview material and documentary evidence is provided to argue that rule-changes are principally the outcome of a dynamic between legislators and players. Players explore the insufficiency of rules in precisely determining conduct in the game, and legislators respond by modifying the rules, to consolidate the game and thereby preserve characteristic features of game-identity and game-viability. This is elaborated by applying Elias's figurational analysis: changes to game rules are conceived as an unintended and unanticipated consequence of powerbalances and the different interests of the functionally interdependent groups who produce game-processes. By virtue of their separate functions in this process, each group seeks to mobilise their power and resources in pursuit of their interests in the game-process. Confirmation for the perspective upon game-constitution developed in this project is sought in an analysis of one contemporary initiative to establish an invasion game as constituted by a set of hybrid rules from Australian Rules and Gaelic Football. This analysis of game-processes and game-rules is designed to both exemplify and inform social theory, and also to make a significant contribution to sociological analysis of the development of contemporary sport.

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The game of Hex is a board game that belongs to the family of Shannon games, which are connection-oriented games where players must secure certain connected components in graphs. The problem of solving Hex is a decision problem complete in PSPACE, which implies that the problem is NP-Hard. Although the Hex problem is difficult to solve, there are a number of problem reduction methods that allow solutions to be found for small Hex boards within practical search limits. The present work addresses two problems, the problem of solving the game of Hex for small board sizes and the problem of creating strong artificial Hex players for larger boards. Recently, a leading Hex solving program has been shown to solve the 7x7 Hex board, but failed to solve 8x8 Hex within practical limits. This work investigates Hex-solving techniques and introduces a series of new search optimizations with the aim to develop a better Hex solver. The most significant of these new optimization techniques is a knowledge base approach that stores and reuses search information to prune Hex-solving searches. This technique involves a generalized form of transposition table that stores game features and uses such features to prove that certain board positions are winning. Experimental results demonstrate a knowledge base Hex solver that significantly speeds up the solving of 7x7 Hex. The search optimization techniques for Hex solvers given here are highly specialized. This work reports on a search algorithm for artificial Hex players, called Pattern Enhanced Alpha-Beta search that can utilize these optimization techniques. On large board positions, an artificial Hex player based on the Pattern Enhanced Alpha- Beta search can return moves in practical times if search depths are limited. Such a player can return a good move provided that the evaluated probabilities of winning on board positions at the depth cut-offs are accurate. Given a large database of Hex games, this work explores an apprenticeship learning approach that takes advantage of this database to derive board evaluation functions for strong Hex playing policies. This approach is compared against a temporal difference learning approach and local beam search approach. A contribution from this work is a method that can automatically generate good quality evaluation functions for Hex players.

vii, 151 leaves / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.) from the Dept. of Mathematics, University of Adelaide, 1972