We study the influence of technological efficiency and organizational inertia on the emergence of competition when firms decide myopically. Using an agent-based computer simulation model, we observe the competitive reaction of a former monopolist to the advent of a new competitor. While the entrant uses a new technology, the monopolist is free either to stick to his former technology or to switch to the new one. We find that?irrespective of details regarding the demand side?a change of industry leadership occurs only if the new (?disruptive?) technology is not too efficient and organizations are inert. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

The future electricity grid is likely to be increasingly complex and uncertain due to the introduction of new technologies in the grid, the increased use of control and communication infrastructure, and the uncertain political climate. In recent years, the transactive energy market framework has emerged as the key framework for future electricity market design in the electricity grid. However, most of the work done in this area has focused on developing retail level transactive energy markets. There seems to be an underlying assumption that wholesale electricity markets are ready to support any retail market design. In this dissertation, we focus on designing wholesale electricity markets that can better support transactive retail market. On the highest level, this dissertation contributes towards developing tools and models for future electricity market designs. A particular focus is placed on the relationship between wholesale markets and investment planning. Part I of this dissertation uses relatively simple models and case studies to evaluate key impediments to flexible transmission operation. In doing so, we identify several potential areas of concern in wholesale market designs: 1. There is a lack of consideration of demand flexibility both in the long-run and in the short-run 2. There is a disconnect between operational practices and investment planning 3. There is a need to rethink forward markets to better manage resource adequacy under long-term uncertainties 4. There is a need for more robust modeling tools for wholesale market design In Part II and Part III of this dissertation, we make use of mathematical decomposition and agent-based simulations to tackle these concerns. Part II of this dissertation uses Benders Decomposition and Lagrangian Decomposition to spatially and temporally decompose a power system and operation problem with active participation of flexible loads. In doing so, we are able to not only improve the computational efficiency of the problem, but also gain various insights on market structure and pricing. In particular, the decomposition suggests the need for a coordinated investment market and forward energy market to bridge the disconnect between operational practices and investment planning. Part III of this dissertation combines agent-based modeling with state-machine based modeling to test various spot, forward, and investment market designs, including the coordinated investment market and forward energy market proposed in Part II of this dissertation. In addition, we test a forward energy market design where 75% of load is required to be purchased in a 2-year-ahead forward market and various transmission cost recovery strategies. We demonstrate how the different market designs result in different investment decisions, winners, and losers. The market insights lead to further policy recommendations and open questions. Overall, this dissertation takes initial steps towards demonstrating how mathematical decomposition and agent-based simulations can be used as part of a larger market design toolbox to gain insights into different market designs and rules for the future electricity grid. In addition, this dissertation identifies market design ideas for further studies, particularly in the design of forward markets and investment cost recovery mechanisms.

The complexities of our surrounding environments are becoming increasingly diverse, more integrated, and continuously more difficult to predict and characterize. These modeling complexities are ever more prevalent in System-of-Systems (SoS) simulations where computational times can surpass real-time and are often dictated by stochastic processes and non-continuous emergent behaviors. As the number of connections continue to increase in modeling environments and the number of external noise variables continue to multiply, these SoS simulations can no longer be explored with traditional means without significantly wasting computational resources. This research develops and tests an adaptive sequential design of experiments to reduce the computational expense of exploring these complex design spaces. Prior to developing the algorithm, the defining statistical attributes of these spaces are researched and identified. Following this identification, various techniques capable of capturing these features are compared and an algorithm is synthesized. The final algorithm will be shown to improve the exploration of stochastic simulations over existing methods by increasing the global accuracy and computational speed, while reducing the number of simulations required to learn these spaces.

In this thesis we presented a new direction for handling missing values in multi agent-based simulation (MABS) at micro-level by using truth tables and logical relations. Although micro-level simulation is a vast field to use logical relations with truth tables to find missing values but it takes values into account at individual levels. We used databases in form of tables to extract missing values. Our literature review suggested us a method for input handling by using electronically saved truth tables. We have defined logical relations according to scenario by interacting with truth tables to find appropriate missing values. Our conclusions suggested a method which can find appropriate values for input parameters when they are missing. Accurate results have been gained according to updated database. In this thesis we have concluded that missing values would be handled in different ways, such as: Artificial neural network, K-nearest neighbor, Statistical method and Data mining; etc… These methods have not facilitated in finding appropriate missing values as we saw in literature. We have created a method that can find missing values and produce good results. We have run our method on a specific scenario to check the efficiency of input handling that motivated us to arrange database in a proper way to handle missing values along.

<div>A transportation system plays a significant role in the economic development of a region by facilitating the movement of goods and services. No matter how well the infrastructure is designed or constructed, it is beneficial to know maintenance needs in the life cycle of the infrastructure so that the service life of the pavement is prolonged by minimizing its life-cycle cost requirements. The pavement life-cycle performance helps maintenance investment decision-makers to efficiently utilize the available infrastructure funds. This research focuses on developing</div><div>a framework for identifying a more effective and efficient way of decision making on the management and maintenance of infrastructure. The developed framework uses an agent-based modeling approach to capture the interaction that exists between different components of the transportation system and their characteristics such as traffic volume and pavement condition, user cost, agency cost, etc. The developed agent-based is useful to investigate the effects of time-varying vehicular density on pavement deterioration and the road users’ driving behavior. The developed framework was demonstrated as a two-lane highway as a case study. Using the developed agent-based simulation framework, it was possible to identify when the road</div><div>infrastructure maintenance should be done to increase the desired PCR value. Also, it was possible to show a decrease in PCR can affect the cost of road users. The framework can track the time to determine when maintenance should be done based on the PCR values that determine whether the</div><div>pavement is in good, moderate, or bad condition. Regardless of the degree of road users’ patience to stay in their travel lanes (patience), the vehicle distribution on the road is balanced in the long run because road users tend to change their travel lanes to minimize their overall travel times. When the patient level is low, road users tend to change lanes more, causing a high number of vehicles on the left lane as it is considered the lane changing lane in two-lane highways. It was also observed that as the patience of the road users increases, the number of vehicles that use the right lane is almost the same as the number of vehicles that use the left lane.</div>

Simulations have considerable potential for the analysis of the evolution of economics systems, a subject often neglected by mainstream economics where the focus is on static equilibria. This thesis investigates the potential of this approach in urban economics. The purpose is to examine how global phenomena emerge from the interactions of economic agents. This is a promising method as a classical economics, lacking the appropriate analytic tools, concentrates on the existence of equilibria and refrains from investigating their stability. This study demonstrates the potential of simulations in three models. Firstly, in a standard model of traffic congestion it is shown that the Nash equilibrium is unstable and cannot be reached dynamically. Secondly, it is shown that simulations of the formation of urban land rents, reproduce elements of the theoretical equilibrium, and also endogenous vacancies, which are an important real-world phenomenon. Thirdly, an agent-based model of the housing market, which reproduces important empirical phenomena such as price dispersion, non-zero search times and vacancies, has been developed. The model provides a basis for the exploration of the complex dynamics of this market.

Dans cette thèse, nous nous intéressons au problème du suivi comportemental des piétons au sein d'un environnement critique partiellement observé. Tandis que plusieurs travaux de la littérature s'intéressent uniquement soit à la position d'un piéton dans l'environnement, soit à l'activité à laquelle il s'adonne, nous optons pour une vue générale et nous estimons simultanément à ces deux données. Les contributions présentées dans ce document sont organisées en deux parties. La première partie traite principalement du problème de la représentation et de l'exploitation du contexte environnemental dans le but d'améliorer les estimations résultant du processus de suivi. L'état de l'art fait mention de quelques études adressant cette problématique. Dans ces études, des modèles graphiques aux capacités d'expressivité limitées, tels que des réseaux Bayésiens dynamiques, sont utilisés pour modéliser des connaissances contextuelles a priori. Dans cette thèse, nous proposons d'utiliser des modèles contextuelles plus riches issus des simulateurs de comportements d'agents autonomes et démontrons l’efficacité de notre approche au travers d'un ensemble d'évaluations expérimentales. La deuxième partie de la thèse adresse le problème général d'influences mutuelles - communément appelées interactions - entre piétons et l'impact de ces interactions sur les comportements respectifs de ces derniers durant le processus de suivi. Sous l'hypothèse que nous disposons d'un simulateur (ou une fonction) modélisant ces interactions, nous développons une approche de suivi comportemental à faible coût computationnel et facilement extensible dans laquelle les interactions entre cibles sont prises en compte. L'originalité de l'approche proposée vient de l'introduction des "représentants'', qui sont des informations agrégées issues de la distribution de chaque cible de telle sorte à maintenir une diversité comportementale, et sur lesquels le système de filtrage s'appuie pour estimer, de manière fine, les comportements des différentes cibles et ceci, même en cas d'occlusions. Nous présentons nos choix de modélisation, les algorithmes résultants, et un ensemble de scénarios difficiles sur lesquels l’approche proposée est évaluée / In this thesis, we are interested in the problem of pedestrian behavioral tracking within a critical environment partially under sensory coverage. While most of the works found in the literature usually focus only on either the location of a pedestrian or the activity a pedestrian is undertaking, we stands in a general view and consider estimating both data simultaneously. The contributions presented in this document are organized in two parts. The first part focuses on the representation and the exploitation of the environmental context for serving the purpose of behavioral estimation. The state of the art shows few studies addressing this issue where graphical models with limited expressiveness capacity such as dynamic Bayesian networks are used for modeling prior environmental knowledge. We propose, instead, to rely on richer contextual models issued from autonomous agent-based behavioral simulators and we demonstrate the effectiveness of our approach through extensive experimental evaluations. The second part of the thesis addresses the general problem of pedestrians’ mutual influences, commonly known as targets’ interactions, on their respective behaviors during the tracking process. Under the assumption of the availability of a generic simulator (or a function) modeling the tracked targets' behaviors, we develop a yet scalable approach in which interactions are considered at low computational cost. The originality of the proposed approach resides on the introduction of density-based aggregated information, called "representatives’’, computed in such a way to guarantee the behavioral diversity for each target, and on which the filtering system relies for computing, in a finer way, behavioral estimations even in case of occlusions. We present the modeling choices, the resulting algorithms as well as a set of challenging scenarios on which the proposed approach is evaluated