Browsing by Subject "Reciprocity"
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Item Business Model Innovation, Social Interactions, and Behavioral Decision Making(2019-08) Pourghannad, BehroozThis thesis consists of three parts. All chapters are centered around the behavioral decision making and business model innovation. In the first essay, we study a supply chain with a supplier selling products to a retailer who is boundedly rational. Under this setting, we study the impacts of the retailer's bounded rationality on the supplier's choice of contract and the supply chain efficiency. We develop a behavioral model that incorporates the human retailers bounded rationality in a supply chain setting. We then conduct a series of laboratory experiments to test whether the model's predictions are still salient even when the supplier is not necessarily rational. In contrast to a supply chain with a fully rational retailer, where a wholesale price contract usually cannot perform better than more complicated nonlinear contracts, we find that when the retailer is boundedly rational a wholesale price contract can dominate commonly used nonlinear contracts such as buy-back and revenue sharing contracts. We characterize the conditions under which a wholesale price contract outperforms more sophisticated non-linear price contracts for the supplier. In both theoretical model and the experiments, we find that a wholesale price contract is more likely to be implemented by the supplier when the supply chain profit margin is low, the retailer is less rational, the demand variance is high, and the retailer's reservation value is high. The results can explain the prevalence of wholesale price contract in business practice when the rationality of retailers cannot always be guaranteed. We also find that the retailer's bounded rationality plays a more important role in determining supply profit than the supplier's bounded rationality. In the second essay, we consider a setting which involves a service provider who sells access to a service or a product to a unit mass of heterogenous consumers. Such s business model is gaining popularity in recent years. With this growth comes opportunities for peer-to-peer trading marketplaces to emerge. However, there is a debate on whether or not peer-to-peer trading of excess capacity is beneficial to service providers and consumers. The second essay in this thesis aims to shed light on this debate and identifies conditions under which the existence of such marketplaces can be a win-win situation for all parties. We develop a game-theoretic model in which consumers participate in a simultaneous coordination game. Consumers are strategic and take into account the opportunity of purchasing or selling extra capacity on the trading market. Our model captures the heterogeneity of consumers' demand and the service provider's ability to modify service plans in view of this trading among consumers. We compare equilibrium outcomes with and without trading and show that outcomes with regard to service provider profit, consumer surplus, and social welfare are crucially dependent on service cost and trading price. A service provider would benefit from trading as long as the trading price is not too low (a low trading price encourages more consumers to opt for the low plan) and the service cost is not too high (a high service cost makes increased consumption due to trading too costly). A trading price that is too low can decrease consumer surplus and social welfare. Hence, a social planner would be interested in inducing a moderate or high trading price. In settings where the service provider can modify prices, consumers are no longer guaranteed to benefit from trading. In this case, trading can hurt consumers if the trading price is either sufficiently high (resulting in consumers paying a higher price for the higher plan) or sufficiently low (resulting in less consumption because more consumers opt for the low plan). Our results provide guidance to service providers, consumers, and policy makers as to when peer-to-peer trading may or may not be beneficial. The results highlight the important interplay between trading price and cost of service in determining various outcomes. For policymakers, the results can be useful in pointing out when such trading improves outcomes for consumers or social welfare and to potential policy levers that could be deployed to affect outcomes. Finally, in the last essay, we study the interaction between information asymmetry and the reciprocity in a financial crowdfunding setting. Most of the crowdfunding platforms encourage entrepreneurs to tap into their social network and bring investors from their social networks to their crowdfunding campaigns. This is done with the intention of creating the early momentum which appears to be the key to running a crowdfunding campaign. However, the incentives and information of those investors who are attracted to crowdfunding campaign from the entrepreneur's social network could be different from other investors who do not have a social tie with the entrepreneur. On the other hand, the regular investors do not have a social tie with the entrepreneur and their sole investment motivation is financial. In the last essay of this thesis, we develop a signaling game to better understand the interaction between the reciprocity and the information flow in a financial crowdfunding setting. Our main result indicates that the reciprocity may create a situation in which the informed investor (those from the entrepreneur's social network) cannot signal their type via distorting her investment.Item Geospatial Economics(2014-06) Johnson, JustinUnderstanding how economic systems interact with ecosystems requires models that include geospatial heterogeneity. Integration of economic and ecological systems is vital for sustainable development, especially with respect to climate change, food security and the management of common property resources. Advances in remote sensing and geographic information systems have created a wealth of data applicable to economics, but it is challenging to incorporate high-resolution, global data in existing economic models. In this thesis, I integrate geospatial data with economic theory to analyze important environmental problems. The next three chapters describe the techniques I use for modeling geospatially-explicit economic systems and apply them to current environmental challenges. Chapter one addresses the tradeoffs between food production and environmental protection. I address the question of how we can optimally feed a growing population (requiring a 100% increase in calorie production by 2050) while minimizing the loss of carbon storage (which is important for mitigating climate change). I use high-resolution, gridded global data to give geospatial specificity to the results of the optimization. The framework I present in this chapter includes only one production choice and one environmental good, but it is more broadly applicable to multiple goods and multiple ecosystem services. This chapter also shows how using geospatial data can increase the policy relevance of an analysis. For example, instead of claiming that tropical forests are in general very valuable, using geospatial data allows for the more precise claim that this specific 10 kilometer patch of forest is better kept as forest than cultivated. Spatially explicit information like this can help construct more specific policies, such as food-for-carbon swaps or identifying which parcels ought to be protected first given a limited conservation budget. Chapter two presents a microeconomic model of spatial foraging that addresses how humans gather goods on a spatially heterogeneous landscape when transport costs are non-negligible. The general problem of foraging arises when multiple agents located in space compete for resources that are characterized by their location. I use agent-based simulation methods to account for agents that must move over the landscape subject to terrain and road networks, depletion by rival agents and spatial heterogeneity with a large number of agents (10 million). The model is applicable to several topics in environmental economics, including fuelwood collection and fisheries management, but also more general economic topics such as housing, employment search, transportation, pollution and urban economics. Another contribution of chapter two is that it makes several methodological advances that allow for spatially-explicit agent-based simulation on extremely large systems. These advances are of two types: first, I present data creation methods that allow for high resolution data to be created globally, relying on satellite-derived data products and spatial downscaling techniques to estimate environmental and social indicators, such as population density or spatially defined wages. Second, I identify computational methods (and implement them in a software application) that allows for fast calculation of agent interactions and movement of space. I discuss the data storage types necessary for this along with a method of vectorizing the calculations to enable computation of extremely large systems (with as many as 10 billion agents). Chapter two concludes an application of the spatial foraging model in which I assess how villagers in Tanzania gather firewood from forests. Firewood collection is a useful example because the need for a spatially explicit model is clear. Transportation costs of firewood are very high relative to their value and firewood in almost always collected by agents foraging for their own consumption. For instance, Fabe and Grote (2013) report that 97.5% of household in Tanzania use firewood as their main fuel, but only 13% of firewood is purchased. I define behavior rules in the simulation based on a microeconomic agricultural household production model (Singh et al. 1986, Bardhan and Udry 1999) apply them to high-resolution geospatial data for Tanzania. I iteratively simulate individual agents' foraging actions and observe the value of firewood obtained. The estimates obtained from this method match existing estimates of firewood collection while providing more detail about where the firewood is collected. The empirical application presented in chapter two is useful to practitioners of ecosystem serve estimation. Calculating the ecosystem service value of non-timber forest products (NTFPs) has been difficult in practice due to computational and theoretical problems. Existing estimation approaches identify the abundance of NTFPs on different forest types but do not explicitly state how agents gather the firewood. The approach I use accounts for how these gathering decisions interact with the abundance of NTFPs to determine the ecosystem service value. The final chapter in this thesis presents a theoretical model that that incorporates reciprocity in a utility maximization model to analyze common-property resource dilemmas. This model, which I refer to as the commons reciprocity utility model, allows agents to make interpersonal comparisons of utility in order to reward cooperators and punish detractors. Extending traditional utility theory in this way is useful to describe the wide-spread observation that individual economic agents do not always free-ride and do not always fall for the tragedy of the commons (Hardin 1968; Ostrom 1990). Although this chapter is primarily focused on theory, I provide two environmental examples to illustrate how it can be applied (including full details in Appendix 2). First, I discuss how international negotiations on climate change can be modeled in this framework by describing each nations' decisions to meet their emissions abatement targets in reciprocal terms. Specifically, nations will reward other nations who do meet their abatement goals and will punish those who do not. I provide a numerical example of this situation that shows increased levels of abatement, higher than the prediction of strong free-riding. Second, I apply the commons reciprocity utility model to a forest commons to explore how agents' decisions to engage in sustainable forestry or to clear-cut the forest depends on the reciprocal relationships of nearby agents. This example is preliminary, but shows how the model can be applied to the agent-based simulation techniques introduced in chapter two. At the deepest level, the goal of this thesis is not to present a complex system of models, but is to answer the question of "how ought we live?" As humans harness an ever-greater portion of available energy and focus it into ever-more complex arrangements, the question of understanding our place in our environment grows more challenging. It requires modeling economic behavior in conjunction with the geospatial landscape on which we act. It is my hope that the methods presented here help condense the nebulous connections among economic and ecological systems into useful bits of truth.Item Humanoid Robot - Human Interaction: Towards Compliance and Reciprocity with a Social Robot Through Completion of a Pregiving Favor(2023-09) Moberg, ReillyUnderstanding the social and natural relationships that humans have with ad-vanced technology is an extremely important consideration in the design and develop- ment of humanoid social robots. By perceiving the social rules within human-human interaction and applying them to human-robot interaction, social influence can lead to participants being more willing and eager to interact with a robot, resulting in the robot being used to its full potential. By combining the work done by Reeves and Nass, 2006 studying the media equa- tion with the social rule of reciprocity (Cialdini, 2008), we suggest that when a robot completes a pregiving favor for a human participant, then the human participant will be influenced by the social rule of reciprocation to comply by the robot’s later request. A phasic, between-subjects experiment (N = 72) using facial electromyography (zygomatic and corrugator) was conducted to learn more about how the natural, hu- man behavior of reciprocation can be applied to human-robot interaction. Measured in this study is the user’s valence of emotions, the user’s willingness to reciprocate a favor, and the measure of compliance based on the number of raffle tickets purchased by the user at the robot’s request. The results suggest that the social rule of recipro- cation exists within human-robot interaction and that when a robot offers a pregiving favor to a person, then that person is more likely to comply with the robot’s later request. In concluding, we discuss theoretical contributions, limitations, and avenues for future research.