China's key forested region is located in the northeast. This region consists of state forest enterprises which manage harvesting and reforestation and have represented the most important source of wood supplies since the 1950s. Deforestation is a major problem there, however, and has resulted in several central government reforms. We develop a framework for assessing the social cost of state forest enterprise deforestation. We first develop a two-principal, one-agent model that fits the federalistic organization state forests, in that state forest managers make (potentially hidden) decisions under influence of provincial and central government policies and quotas meant to direct manager behavior. This model is used to derive an expression of the social cost of these hidden actions as well as a comparison of first and second best government policies. We then use panel data from a survey conducted by the Environmental Economics Program in China (EEPC) to compute social welfare losses and use a regression approach to confirm the main factors in these costs in practice. A sensitivity analysis shows that lower harvesting limits and a more accurate monitoring system are the keys to lowering social welfare loss. These are more important than conventional instruments used by the governments such as wages for managers that achieve certain targets. Through regression analysis we find that the remote areas with a higher percentage of mature natural forests are the ones that will always have the highest social welfare loss. These areas are the hardest to monitor, but our results show they must be a critical focus moving forward. / M.S. / China's key forested region is located in the northeast. This region consists of state forest enterprises which manage harvesting and reforestation and have represented the most important source of wood supplies since the 1950s. Deforestation is a major problem there. We develop a framework for assessing the damage to the society because of deforestation. We develop a theoretical model to describe the forest management structure, in which state forest managers make (potentially hidden) decisions under influence of provincial and central government policies. This model is used to derive an expression of the damage. We then use data from a survey conducted by the Environmental Economics Program in China (EEPC) to compute the damage and confirm the main factors in these damages in practice. We find that lower harvesting limits and a more accurate monitoring system are the keys to lowering the damage. These are more important than conventional instruments used by the governments such as wages for managers that achieve certain targets. We also find that the remote areas with a higher percentage of mature natural forests are the ones that will always have the largest damage. These areas are the hardest to monitor, but our results show they must be a critical focus moving forward.

A "complex" system typically has a relatively large number of dynamically interacting components and tends to exhibit emergent behavior that cannot be explained by analyzing each component separately. A biological neural network is one example of such a system. A multi-agent model of such a network is developed to study the relationships between a network's structure and its spike train output. Using this model, inferences are made about the synaptic structure of networks through cluster analysis of spike train summary statistics A complexity measure for the network structure is also presented which has a one-to-one correspondence with the standard time series complexity measure sample entropy.

<p>Principal agent theory is widely used to model supply chain relationship, in which a supplier is the agent and a manufacturer is the principal. Both the manufacturer and supplier can influence product quality and consequentially share costs of product failures. Rich theoretical results under the principal agent model framework have been accumulated in the last two decades, but empirical evidence on whether the Stackelberg’s leadership game truly imitates practical supply chain relationship remains unfound. We study the domestic automobile industry in the last decade and provides to our best knowledge the first empirical evidence to assess the validity and practicality of principal agent theory and draw the implications of principal agent theory on supply chain relationship costs. Our empirical results suggest that Japanese OEMs behave more like principal agent theory suggests than the US OEMs in general and thus gain significant benefits in terms of marginal effort costs in motivating suppliers’ quality improvement behaviors and reducing overall manufacturer’s quality costs. Specifically, Toyota behaves closest to the optimal solution in the principal agent theory and therefore has the lowest manufacturer effort costs in improving product quality and achieves the overall lowest manufacturer’s quality costs in supply chain. Honda and Nissan are ranked 2^nd and 3^rd in terms of principal agent behaviors, but their marginal quality improvement effort costs are 33% and 61% higher than Toyota, and their total manufacturer’s quality costs are both around 17% higher compared to industrial leader Toyota by our estimate. US OEMs GM, Ford and Chrysler are believed to behave inconsistent to principal agent theory suggest, and consequently suffer a much higher marginal effort cost in motivating supplier’s quality improvement than Toyota as well as the overall manufacturer’s quality costs. GM and Ford are estimated doubled marginal effort costs than Toyota, and Chrysler is even higher at 1.6 times. GM’s overall manufacturer’s quality cost is 24% higher than Toyota, Ford is around 31% higher and Chrysler is around 48% higher. Our analysis gives a new perspective from principal agent theory to explain why Japanese OEMs especially Toyota has a better supply chain quality costs than US OEMs as literature and consensus suggested. In addition, we contribute in literature by linking the principal agent theory with automotive industrial data and first ever empirically validate the legitimacy of principal agent theory in modeling manufacturer-supplier relationship and quantitatively derive practical conclusions on marginal effort costs and manufacturer’s total supply chain quality cost implications. To guarantee the robustness of the empirical results, various sensitivity analyses are conducted and our main conclusions remain unchanged. </p>

Many venture capitalists (VCs) use the “VC method” of valuation where they use judgment to estimate a probability of successful exit while determining the ownership share to demand in exchange for investing in a venture. However, prior models are not aligned with the “VC method” because they do not consider private information about entrepreneurial characteristics, the primary drivers of the above probability, and consequently do not model judgment. The three main chapters of this thesis—one theoretical, one simulation, and one empirical study—examine the venture capital deal process in sync with the “VC method.” Chapter 2 is theoretical and develops a principal-agent model of venture capital deal process incorporating double-sided moral hazard and one-sided private information. The VC is never fully informed about the entrepreneur’s disutility of effort in spite of due diligence checks, so takes on a belief about the latter’s performance in the funded venture to determine the offer. This study suggests that there exists a critical point in the VC’s belief—and correspondingly in the VC’s ownership share—that maximizes the total return to the two parties. It also uncovers optimal revision strategies for the VC to adopt if the offer is rejected where it is shown that the VC should develop a strong advisory capacity and minimize time constraints to facilitate investment. Chapter 3 simulates venture capital deals as per the theoretical model and confirms the existence of critical points in the VC’s belief and ownership share that maximize the returns to the two parties and their total return. Particularly, the VC’s return (in excess of his or her return from an alternate investment) peaks for a moderate ownership share for the VC. Since private information with the entrepreneur would preclude the VC from knowing these critical points a priori, the VC should demand a moderate ownership share to stay close to such a peak. Using data from simulations, we also generate predictions about the properties of the venture capital deal space—notably: (a) Teamwork is crucial to financing; and (b) If the VC is highly confident about the entrepreneur’s performance, it would work to the latter’s advantage. Chapter 4 reports the results from our survey of eight seasoned VCs affiliated with seven firms operating in Canada, USA, and UK, where our findings received a high degree of support.

Many venture capitalists (VCs) use the “VC method” of valuation where they use judgment to estimate a probability of successful exit while determining the ownership share to demand in exchange for investing in a venture. However, prior models are not aligned with the “VC method” because they do not consider private information about entrepreneurial characteristics, the primary drivers of the above probability, and consequently do not model judgment. The three main chapters of this thesis—one theoretical, one simulation, and one empirical study—examine the venture capital deal process in sync with the “VC method.” Chapter 2 is theoretical and develops a principal-agent model of venture capital deal process incorporating double-sided moral hazard and one-sided private information. The VC is never fully informed about the entrepreneur’s disutility of effort in spite of due diligence checks, so takes on a belief about the latter’s performance in the funded venture to determine the offer. This study suggests that there exists a critical point in the VC’s belief—and correspondingly in the VC’s ownership share—that maximizes the total return to the two parties. It also uncovers optimal revision strategies for the VC to adopt if the offer is rejected where it is shown that the VC should develop a strong advisory capacity and minimize time constraints to facilitate investment. Chapter 3 simulates venture capital deals as per the theoretical model and confirms the existence of critical points in the VC’s belief and ownership share that maximize the returns to the two parties and their total return. Particularly, the VC’s return (in excess of his or her return from an alternate investment) peaks for a moderate ownership share for the VC. Since private information with the entrepreneur would preclude the VC from knowing these critical points a priori, the VC should demand a moderate ownership share to stay close to such a peak. Using data from simulations, we also generate predictions about the properties of the venture capital deal space—notably: (a) Teamwork is crucial to financing; and (b) If the VC is highly confident about the entrepreneur’s performance, it would work to the latter’s advantage. Chapter 4 reports the results from our survey of eight seasoned VCs affiliated with seven firms operating in Canada, USA, and UK, where our findings received a high degree of support.

Multi-target incentive contracts are widely observed in practice to stimulate salesforce effort. However, little is known about their effectiveness and the issues involved in designing them. In this thesis, we investigate the incentive contracting problem between a manufacturer and an agent when the realized sales of a product are affected by both the agent's selling effort and the type of the agent. The agent's type is uncertain to the manufacturer, whereas the agent can observe the actual type when exerting her selling effort. Again, this is unobservable by the manufacturer. For contract design problem, we develop a principal-agent model with both moral hazard and adverse selection. We examine the manufacturer's optimal contract parameter decisions employing a single multi-target contract for the agent who can be of different types. Because menu contracts are commonly studied in literature for the adverse selection problem, we also study a menu of single-target contracts; and examine the manufacturer's optimal contract parameter decisions. We then compare the performance between the two types of contract. We arrive at a number of managerial insights regarding the design and the performance of multi-target contract and menu contract.

This work deals with modeling of the human psyche and its cognitive processes, emotions and behaviors based on agent system. The first objective of this examines the issue from the perspective of general psychology and intelligent systems. The work discusses in detail the design model of the human psyche with definitions of agents system and design of simulation environment. The special part is devoted to the design of the neural network system. After description of implementation of the model and simulation environment, finally, the work show results of experiments with the implemented system from the perspective of conformity with psychological theories and benefits to artificial intelligence.

L'assolement' c'est à dire l'attribution des cultures sur les différentes parcelles de l'exploitation agricole' est un élément fondamental de la stratégie de l'agriculteur. Il provient d'un équilibre entre des contraintes et objectifs multiples alliant les dimensions sociale' économique' agronomique et environnementale (utilisation des ressources). Il reflète deux dimensions: une dimension stratégique (lié aux orientations de l'exploitation) et une dimension tactique plus lié à l'adaptation des attributions suite à des contextes changeant (climatique' prix). Afin représenter ces deux niveaux et ainsi structurer les enjeux relatifs aux choix d'assolement à l'échelle de l'exploitation nous proposons une approche basées sur : i) une analyse du processus décisionnel par le biais d'enquêtes en exploitation; ii) une modélisation dynamique aussi bien sur des processus biophysiques (modèles de culture) que sur des processus de gestion (modèle de décision) pour comprendre et simuler. / Evolutions of the institutional and environmental contexts are driving search for alternative cropping systems to reduce water use while maintaining high levels of productivity. This thesis is an account of the long tradition of research on cropping-plan choices at the farm level. It concerns the scope of modelling agricultural systems with an opening to economy. The objective of the research described in this thesis is to produce formalised knowledge on farmers’ croppingplan choices under uncertain environment (price and weather) by analysing and modelling their decision-making processes. Formalising and modelling decision-making processes is becoming a crucial point to develop decision support systems that go beyond limitations of formerly developed prescriptive approaches. This thesis contributes to the development of a formalised and integrated methodology to study and model complex decision-making process. This methodology enables to fill the gap between field surveys and decision-model implementation. The methodology is drawn upon a theoretical background of the decision-making' and consistently combined tools to respectively survey' analyse' model and implement coupled agent and biophysical models. In this thesis' I address the question of uncertainty in two directions. I first analyse the spatio-temporal dynamic of individual farmers’ decision-making process. Then I estimate farmers’ aversion to risk by comparing stated and revealed elicitation methods. On the basis of field survey results' I develop a decision model called CRASH. The approach to develop the model stresses on explicit formalisation of the decision-making process in its temporal and spatial dimensions' and representation of the domain knowledge through generic concepts that are close to ones used by decision-makers. The implementation of developed models is carried out on the RECORD platform as trail blazing project. The originality relies on the use of dynamic models on both the biophysical and management processes. This research opens new perspectives for developing farm specific decision support systems that are based on simulating farmers’ decisionmaking processes. The modelling and simulating the cropping-plan decision-making processes should enable to design with farmers cropping systems that re-conciliate the required adaptive capacities and needs to maintain cropping systems productivity.

Diese Arbeit enthält drei unabhängige Kapitel, jedes davon im Bereich der Dynamischen Vertragstheorie. Kapitel I zeigt, dass deterministische dynamische Prinzipal-Agenten-Verträge immer mindestens so ertragreich sind wie stochastische, falls die sogenannte Methode erster Ordnung des dynamischen Mechanismus-Designs erfüllt ist. Meine Ergebnisse legen dar, dass die in der Literatur übliche Einschränkung auf deterministische Verträge zulässig ist, so lange die Methode erster Ordnung gültig ist. Kapitel II basiert auf einer gemeinsamen Arbeit mit Ilia Krasikov und Rohit Lamba. Ein Großanbieter (Prinzipal) handelt mit einer kleinen Firma (Agent) einen wiederkehrenden Geschäftsvertrag aus, wobei sich der Agent als ungeduldiger erweist. Der optimale Vertrag wird durch zwei Schlüsseleigenschaften beschrieben: Neustart und Abbruch, die vielerlei Eigenschaften der angebotenen Verträge darlegen. Kapitel III basiert schließlich auf einer gemeinsamen Arbeit mit Rohit Lamba. Darin besitzt der Agent dynamische private Information, die einem Markovprozess folgt. Ein monopolistischer Prinzipal verkauft dem Agenten ein nicht-dauerhaftes Gut und er verpflichtet sich in jeder Periode an den ursprünglich ausgehandelten Vertrag. Die entstehenden Informationskosten verhindern erst-beste Verträge bei auftretender Persistenz im Typ des Agenten. Diese Arbeit stellt einen Weg bereit, wie man den optimalen deterministischen Vertrag in dynamischen Prinzipal-Agenten-Modellen erhält. Der gewöhnliche Weg mit lediglich lokal nach unten bindenden Anreizverträglichkeitsbedingungen misslingt bei hoher Persistenz der Typrealisierungen und bei stark differenzierender Diskontierung. Zudem zeigt die Arbeit wann mit Gewissheit stochastische Verträge ausgeschlossen werden können. / This dissertation consists of three independent chapters, each in the field of dynamic contracting. Chapter I shows that deterministic dynamic contracts between a principal and an agent are always at least as profitable to the principal as stochastic ones, if the so-called first-order approach in dynamic mechanism design is satisfied. My results demonstrate that the usual restriction in the literature to deterministic contracts is admissible, as long as the first-order approach is valid. Chapter II is based on joint work with Ilia Krasikov and Rohit Lamba. We consider a large supplier (principal) who contracts with a small firm (agent) to repeatedly provide working capital in return for payments. The agent is less patient than the principal. The optimal contract is characterized by two key properties: restart and shutdown, which capture various aspects of contracts offered in the marketplace. Finally, Chapter III is based on joint work with Rohit Lamba. We consider the problem of optimal contracting where the agent has dynamic private information, which follows a Markov process. In each period, a monopolistic principal sells a nondurable good to the agent and she commits to the contract she made in the initial period. The emerging information costs prevent first-best contracts whenever there is persistency in the agent’s type. This thesis provides a strategy on how to obtain the optimal deterministic contract in dynamic principal-agent models with Markovian type realizations. We see that the usual approach with only local downward binding incentive compatibility constraints does not work for highly persistent type realizations and for large differences in discounting. Furthermore, I show in which situations we with certainty can exclude stochastic contracts.

Mit der hier vorliegenden Arbeit wird ein mikroökonomisches Multiagentenmodell eines Produktionssektors vorgeschlagen. Das Modell folgt einem post-walrasianischem Ungleichgewichtsansatz und beschreibt optimierende Agenten der Produktionsseite. Diese berücksichtigen in probabilistischen Nebenbedingungen Risiken des Cash Flow, die sich aus unsicheren Absatzmengen ergeben. Produzenten stehen in monopolistischer Konkurrenz und lernen durch Beobachten. Wird vorliegendes Modell in ein Totalmodell integriert, so wird es möglich, die sich aus der Klimadebatte ergebenden, notwendigen Veränderungen im Investitions- und Produktionsverhalten zu diskutieren und darzustellen. / In the following thesis I propose a microeconomic Multi-Agent-Model of a production sector. I apply a Post-Walrasian disequilibrium approach and describe optimizing agents. These agents use chance constraints which depict a Cash Flow at Risk approach. Agents act in a Monopolistic-Competition environment.