Understanding the Internet's relevance to media ownership policy: a model of too many choices

Does the Internet provide a failsafe against media consolidation in the wake of an easing of media ownership rules? This paper posits a model of news outlet selection on the Internet in which consumers experience cognitive costs that increase with the number of options faced. Consistent with psychological evidence, these costs may be reduced by constraining one’s choice set to “safe bets” familiar from offline (e.g., CNN.com). It is shown that, as the number of outlets grows, dispersion of consumer visitation across outlets inevitably declines. Consequently, independent Internet outlets may fail to mitigate lost outlet independence on other media.Choice framing; Media ownership; Internet; Differentiated products; Location models

The Maximal Payoff and Coalition Formation in Coalitional Games

This paper first establishes a new core theorem using the concept of generated payoffs: the TU (transferable utility) core is empty if and only if the maximum of generated payoffs (mgp) is greater than the grand coalition’s payoff v(N), or if and only if it is irrational to split v(N). It then provides answers to the questions of what payoffs to split, how to split the payoff, what coalitions to form, and how long each of the coalitions will be formed by rational players in coalitional TU games. Finally, it obtains analogous results in coalitional NTU (non-transferable utility) games.Coalition Formation, Core, Maximal Payoff, Minimum No-Blocking Payoff

A REVIEW OF ALTERNATIVE EXPECTATIONS REGIMES IN COMMODITY MARKETS: SPECIFICATION, ESTIMATION, AND HYPOTHESIS TESTING USING STRUCTURAL MODELS

Price expectations play a critical role in commodity markets where producers must make input decisions well before output is realized. This paper brings together alternative expectations regimes, their estimation, and hypothesis tests for use in structural commodity models to determine their use by commodity producers. Extrapolative mechanisms and rational expectations are considered under risk neutrality and risk aversion. The assumptions implicit in the use of aggregate data in these models are made explicit. Structural models using individual survey data are discussed. While Muth's rational expectations hypothesis has found widespread acceptance in the macroeconomic literature, empirical results from industry studies indicate that commodity producers may have heterogeneous price expectations, with no single expectations hypothesis dominating. This is not surprising given that different producers possess different information and have different costs associated with information collection and processing.Demand and Price Analysis,

Dynamic Models for International Environmental Agreements

In this paper we develop a model to analyze, in a dynamic framework, how countries join international environmental agreements (IEAs). In the model, where countries suffer from the same environmental damage as a result of the total global emissions, a non-signatory country decides its emissions by maximizing its own welfare, whereas a signatory country decides its emissions by maximizing the aggregate welfare of all signatory countries. Signatory countries are assumed to be able to punish the non-signatories at a cost. When countries decide on their pollution emissions they account for the evolution of the pollution over time. Moreover, we propose a mechanism to describe how countries reach a stable IEA. The model is able to capture situations with partial cooperation in an IEA stable over time. It also captures situations where all countries participate in a stable agreement, or situations where no stable agreement is feasible. When more than one possibility coexists, the long-term outcome of the game depends on the initial conditions (i.e. the size of the initial group of signatory countries and the pollution level).International Environmental Agreements, Non-Cooperative Dynamic Game, Coalition Stability

Hoping for A to Z While Rewarding Only A: Complex Organizations and Multiple Goals

This paper explores the trade-offs inherent in the pursuit and fulfillment of multiple performance goals in complex organizations. We examine two related research questions: (1) What are the organizational implications of pursuing multiple performance goals? (2) Are local and myopic (as opposed to global) goal prioritization strategies effective in dealing with multiple goals? We employ a series of computational experiments to examine these questions. Our results from these experiments both formalize the intuition behind existing wisdom and provide new insights. We show that imposing a multitude of weakly correlated performance measures on even simple organizations (i.e., an organization comprised of independent employees) leads to a performance freeze in that actors are not able to identify choices that enhance organizational performance across the full array of goals. This problem increases as the degree of interdependence of organizational action increases. We also find that goal myopia, spatial differentiation of performance goals, and temporal differentiation of performance goals help rescue organizations from this status quo trap. In addition to highlighting a new class of organizational problems, we argue that in a world of boundedly rational actors, incomplete guides to action in the sense of providing only a subset of underlying goals prove more effective at directing and coordinating behavior than more complete representations of underlying objectives. Management, in the form of the articulation of a subset of goals, provides a degree of clarity and focus in a complex world

Finding Core Members of a Hedonic Game

Agent-based modeling (ABM) is a frequently used paradigm for social simulation; however, there is little evidence of its use in strategic coalition formations. There are few models that explore coalition formation and even fewer that validate their results against an expected outcome. Cooperative game theory is often used to study strategic coalition formation but solving games involving a significant number of agents is computationally intractable. However, there is a natural linkage between ABM and the study of strategic coalition formation. A foundational feature of ABM is the interaction of agents and their environment. Coalition formation is primarily the result of interactions between agents to form collective groups. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro level effect without large computational requirements. This research proposes a hybrid model combining Agent-based modeling and cooperative game theory to find members of a cooperative game’s solution. The algorithm will be applied to the core solution of hedonic games. The core solution is the most common solution set. Hedonic games are a subset of cooperative games whereby agents’ utilities are defined solely by a preference relation over the coalitions of which they are members. The utility of an agent is non-transferrable; there can be no transfer, wholly or in part, of the utility of one agent to another. Determining the core of a hedonic game is NP-complete. The heuristic algorithm utilizes the stochastic nature of ABM interactions to minimize computational complexity. The algorithm has seven coalition formation functions. Each function randomly selects agents to create new coalitions; if the new coalition improves the utility of the agents, it is incorporated into the coalition structure otherwise it is discarded. This approach reduces the computational requirements. This work contributes to the modeling and simulation body of knowledge by providing researchers with a generalized ABM algorithm for forming strategic coalition structures. It provides an empirically validated model based on existing theory that utilizes sound mathematics to reduce the computational complexity and demonstrates the advantages of combining strategic, analytical models with Agent-based models for the study of coalition formation

An evolutionary theory of systemic risk and its mitigation for the global financial system

This thesis is the outcome of theory development research into an identified gap in knowledge about systemic risk of the global financial system. It takes a systems-theoretic approach, incorporating a simulation-constructivist orientation towards the meaning of theory and theory development, within a realist constructivism epistemology for knowledge generation about complex social phenomena. The specific purpose of which is to describe systemic risk of failure, and explain how it occurs in the global financial system, in order to diagnose and understand circumstances in which it arises, and offer insights into how that risk may be mitigated. An outline theory is developed, introducing a new operational definition of systemic risk of failure in which notions from evolutionary economics, finance and complexity science are combined with a general interpretation of entropy, to explain how catastrophic phenomena arise in that system. When a conceptual model incorporating the Icelandic financial system failure over the years 2003 – 2008 is constructed from this theory, and the results of simulation experiments using a verified computational representation of the model are validated with empirical data from that event, and corroborated by theoretical triangulation, a null-hypothesis about the theory is refuted. Furthermore, results show that interplay between a lack of diversity in system participation strategies and shared exposure to potential losses may be a key operational mechanism of catastrophic tensions arising in the supply and demand of financial services. These findings suggest new policy guidance for pre-emptive intervention calls for improved operational transparency from system participants, and prompt access to data about their operational behaviour, in order to prevent positive feedback inducing a failure of the system to operate within required parameters. The theory is then revised to reflect new insights exposed by simulation, and finally submitted as a new theory capable of unifying existing knowledge in this problem domain

Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid. Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating. In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023

Agent-based models to couple natural and human systems for watershed management analysis

This dissertation expands conventional physically-based environmental models with human factors for watershed management analysis. Using an agent-based modeling framework, two approaches, one based on optimization and the other on data mining-are applied to modeling farmers' pumping decision-making processes in the High Plains aquifer within the hydrological observatory area. The resulting agent-based models (ABMs) are coupled with a physically-based groundwater model to investigate the interactions between farmers and the underlying groundwater system. With the optimization-based approach, the computational intensity arises from the execution of the resulting coupled ABM and groundwater model. This dissertation develops a computational framework that utilizes multithreaded programming and Hadoop-based cloud computing to address the computational issues. The framework allows multiple users to access and execute the web-based application of the coupled models simultaneously without an increase in latency via computer network. In addition, another computational framework to combine Hadoop-based Cloud Computing techniques with Polynomial Chaos Expansion (PCE) based variance decomposition approach is developed to conduct global sensitivity analysis with the coupled models, and influential behavioral parameters which are used to simulate agents’ behavior are identified. Being different from the optimization-based approach, which assumes all agents are rational, the data-driven approach attempts to account for the influences of agents’ bounded rationality on their behavior. A directed information graph (DIG) algorithm is used to exploit the causal relationships between agents’ decisions (i.e., groundwater irrigation depth) and time-series of environmental, socio-economical and institutional variables, and a machine learning technique, boosted regression tree (BRT) is applied to converting these causal relationships to agents’ behavioral rules. It is found that, in comparison with the optimization-based approach, crop profits and water tables as the result of agents’ pumping behavior derived using the data-driven approach can better mimic the actual observations. Thus, we can conclude that the data-driven approach using DIG and BRT outperforms the optimization-based approach when capturing agents’ pumping behavioral uncertainty as the result of bounded rationality, and for simulating real-world behaviors of agents