Braess's Paradox in an Agent-based Transport Model

Braess's paradox states that adding a link to the network can increase total travel time in a user equilibrium. In this paper, Braess's paradox is analyzed in the agent-based transport simulation MATSim. It can be observed, that two different types of the paradox occur: In the absence of spill back effects, the delay per agent caused by adding a new link is bounded, i.e. the delay per agent will not increase by extending the time span during which agents depart and, therefore, increasing the number of agents. In the presence of spill back effects, the delay per agent is unbounded. The same holds for the price of anarchy in both cases which gets unbounded if spill back effects are considered. As a consequence, Braess's paradox tends to be underestimated in models that do not capture spill back effects

Crowd dynamics

Crowd dynamics are complex. This thesis examines the nature of the crowd and its dynamics with specific reference to the issues of crowd safety. A model (Legion) was developed that simulates the crowd as an emergent phenomenon using simulated annealing and mobile cellular automata. We outline the elements of that model based on the interaction of four parameters: Objective, Motility, Constraint and Assimilation. The model treats every entity as an individual and it can simulate how people read and react to their environment in a variety of conditions. Which allows the user to study a wide range of crowd dynamics in different geometries and highlights the interactions of the crowd with their environment. We demonstrate that the model runs in polynomial time and can be used to assess the limits of crowd safety during normal and emergency egress. Over the last 10 years there have been many incidents of crowd related disasters. We highlight deficiencies in the existing guidelines relating to crowds. We compare and contrast the model with the safety guidelines and highlight specific areas where the guides may be improved. We demonstrate that the model is capable of reproducing these dynamics without additional parameters, satisfying Occam's Razor. The model is tested against known crowd dynamics from field studies, including Wembley Stadium, Balham Station and the Hong Kong Jockey club. We propose an alternative approach to assessing the dynamics of the crowd through the use of the simulation and analysis of least effort behaviour. Finally we test the model in a variety of applications where crowd related incidents warrant structural alterations at client sites. We demonstrate that the model explains the variance in a variety of field measurements, that it is robust and that it can be applied to future designs where safety and crowd comfort are criteria for design and cost savings

Tools of Game Theory in Economic Decision Making

Tato práce se zabývá současnými trendy v aplikaci teorie her k tvorbě ekonomických modelů, které se následně využívají při ekonomickém rozhodování s podporou prostředků informatiky. Práce se zejména opírá o poznatky teorie statických a dynamických her a her s dokonalými a nedokonalými informacemi. Zkoumány jsou modely týkající se sdílení zdrojů, aukcí a managementu. Pro každý z popsaných modelů je prezentována konkrétní aplikace.This thesis deals with the present trends in application of game theory to creation of economic models, which are subsequently used in economic decision making with the support of tools of information technology. It mainly focuses on the tools of static and dynamic games and games with perfect and imperfect information. Models of resource sharing, auctions and management are under investigation. For each of the described models a concrete application is presented.

Dynamic traffic congestion pricing mechanism with user-centric considerations

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 85-95).In this thesis, we consider the problem of designing real-time traffic routing systems in urban areas. Optimal dynamic routing for multiple passengers is known to be computationally hard due to its combinatorial nature. To overcome this difficulty, we propose a novel mechanism called User-Centric Dynamic Pricing (UCDP) based on recent advances in algorithmic mechanism design. The mechanism allows for congestion-free traffic in general road networks with heterogeneous users, while satisfying each user's travel preference. The mechanism first informs whether a passenger should use public transportation or the road network. In the latter case, a passenger reports his maximum accepted travel time with a lower bound announced publicly by the road authority. The mechanism then assigns the passenger a path that matches with his preference given the current traffic condition in the network. The proposed mechanism introduces a fairness constrained shortest path (FCSP) problem with a special structure, thus enabling polynomial time computation of path allocation that maximizes the sequential social surplus and guarantees fairness among passengers. The tolls of paths are then computed according to marginal cost payments. We show that reporting true preference is a weakly dominant strategy. The performance of the proposed mechanism is demonstrated on several simulated routing experiments in comparison to user equilibrium and system optimum.by Kim Thien Bui.S.M. in Transportatio

Self-Organized Dynamics of Power Grids: Smart Grids, Fluctuations and Cascades

Climate change is one of the most pressing issues of our time and mitigating it requires a reduction of CO2 emissions. A big step towards achieving this goal is increasing the share of renewable energy sources, as the energy sector currently contributes 35% to all greenhouse gas emissions. However, integrating these renewable energy sources challenges the current power system in two major ways. Firstly, renewable generation consists of more spatially distributed and smaller power plants than conventional generation by nuclear or coal plants, questioning the established hierarchical structures and demanding a new grid design. Restructuring becomes necessary because wind and solar plants have to be placed at favorable sites, e.g., close to coasts in the case of wind. Secondly, renewables do not provide a deterministic and controllable power output but introduce power fluctuations that have to be controlled adequately. Many solutions to these challenges are build on the concept of smart grids, which require an extensive information technology (IT) infrastructure communicating between consumers and generators to coordinate efficient actions. However, an intertwined power and IT system raises great privacy and security concerns. Is it possible to forgo a large IT infrastructure in future power grids and instead operate them purely based on local information? How would such a decentrally organized system work? What is the impact of fluctuation on short time scales on the dynamical stability? Which grid topologies are robust against random failures or targeted attacks? This thesis aims to establish a framework of such a self-organized dynamics of a power grid, analyzing its benefits and limitations with respect to fluctuations and discrete events. Instead of a centrally monitored and controlled smart grid, we propose the concept of Decentral Smart Grid Control, translating local power grid frequency information into actions to stabilize the grid. This is not limited to power generators but applies equally to consumers, naturally introducing a demand response. We analyze the dynamical stability properties of this framework using linear stability methods as well as applying numerical simulations to determine the size of the basin of attraction. To do so, we investigate general stability effects and sample network motifs to find that this self-organized grid dynamics is stable for large parameter regimes. However, when the actors of the power grid react to a frequency signal, this reaction has to be sufficiently fast since reaction delays are shown to destabilize the grid. We derive expressions for a maximum delay, which always desynchronizes the system based on a rebound effect, and for destabilizing delays based on resonance effects. These resonance instabilities are cured when the frequency signal is averaged over a few seconds (low-pass filter). Overall, we propose an alternative smart grid model without any IT infrastructure and analyze its stable operating space. Furthermore, we analyze the impact of fluctuations on the power grid. First, we determine the escape time of the grid, i.e., the time until the grid desynchronizes when subject to stochastic perturbations. We simulate these events and derive an analytical expression using Kramer's method, obtaining the scaling of the escape time as a function of the grid inertia, transmitted power, damping etc. Thereby, we identify weak links in networks, which have to be enhanced to guarantee a stable operation. Second, we collect power grid frequency measurements from different regions across the world and evaluate their statistical properties. Distributions are found to be heavy-tailed so that large disturbances are more common than predicted by Gaussian statistics. We model the grid dynamics using a stochastic differential equation to derive the scaling of the fluctuations based on power grid parameters, identifying effective damping as essential in reducing fluctuation risks. This damping may be provided by increased demand control as proposed by Decentral Smart Grid Control. Finally, we investigate discrete events, in particular the failure of a single transmission line, as a complementary form of disturbances. An initial failure of a transmission line leads to additional load on other lines, potentially overloading them and thereby causing secondary outages. Hence, a cascade of failures is induced that propagated through the network, resulting in a large-scale blackout. We investigate these cascades in a combined dynamical and event-driven framework, which includes transient dynamics, in contrast to the often used steady state analysis that only solves static flows in the grid while neglecting any dynamics. Concluding, we identify critical lines, prone to cause cascades when failing, and observe a nearly constant speed of the propagation of the cascade in an appropriate metric. Overall, we investigate the self-organized dynamics of power grids, demonstrating its benefits and limitations. We provide tools to improve current grid operation and outline a smart grid solution that is not reliant on IT. Thereby, we support establishing a 100% renewable energy system

Essays in economic development and political economy

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 183-197).This thesis examines three topics. The first chapter, entitled "Persistent Effects of Peru's Mining Mita" utilizes regression discontinuity to examine the long-run impacts of the mita, an extensive forced mining labor system in effect in Peru and Bolivia between 1573 and 1812. Results indicate that a mita effect lowers household consumption by around 25% and increases the prevalence of stunted growth in children by around six percentage points in subjected districts today. Using data from the Spanish Empire and Peruvian Republic to trace channels of institutional persistence, I show that the mita's influence has persisted through its impacts on land tenure and public goods provision. Mita districts historically had fewer large landowners and lower educational attainment. Today, they are less integrated into road networks, and their residents are substantially more likely to be subsistence farmers. The second chapter, entitled "Trafficking Networks and the Mexican Drug War" examines how drug traffickers' economic objectives influence the direct and spillover effects of Mexican policy towards the drug trade. Drug trade-related violence has escalated dramatically in Mexico during the past five years, claiming over 40,000 lives. By exploiting variation from close mayoral elections and a network model of drug trafficking, the study develops three sets of results. First, regression discontinuity estimates show that drug trade-related violence in a municipality increases substantially after the close election of a mayor from the conservative National Action Party (PAN), which has spearheaded the war on drug trafficking. This violence consists primarily of individuals involved in the drug trade killing each other. The empirical evidence suggests that the violence reflects rival traffickers' attempts to wrest control of territories after crackdowns initiated by PAN mayors have challenged the incumbent criminals. Second, the study predicts the diversion of drug traffic following close PAN victories by estimating a model of equilibrium routes for trafficking drugs across the Mexican road network to the U.S. When drug traffic is diverted to other municipalities, drug trade-related violence in these municipalities increases. Moreover, female labor force participation and informal sector wages fall, corroborating qualitative evidence that traffickers extort informal sector producers. Finally, the study uses the trafficking model and estimated spillover effects to examine the allocation of law enforcement resources. Overall, the results demonstrate how traffickers' economic objectives and constraints imposed by the routes network affect the policy outcomes of the Mexican Drug War. The third chapter, entitled "Insurgency and Long-Run Development: Lessons from the Mexican Revolution" exploits within-state variation in drought severity to identify how insurgency during the Mexican Revolution, a major early 20th century armed conflict, impacted subsequent government policies and long-run economic development. Using a novel municipal-level dataset on revolutionary insurgency, the study documents that municipalities experiencing severe drought just prior to the Revolution were substantially more likely to have insurgent activity than municipalities where drought was less severe. Many insurgents demanded land reform, and following the Revolution, Mexico redistributed over half of its surface area in the form of ejidos: farms comprised of individual and communal plots that were granted to a group of petitioners. Rights to ejido plots were non-transferable, renting plots was prohibited, and many decisions about the use of ejido lands had to be countersigned by politicians. Instrumental variables estimates show that municipalities with revolutionary insurgency had 22 percentage points more of their surface area redistributed as ejidos. Today, insurgent municipalities are 20 percentage points more agricultural and 6 percentage points less industrial. Incomes in insurgent municipalities are lower and alternations between political parties for the mayorship have been substantially less common. Overall, the results support the hypothesis that land reform, while successful at placating insurgent regions, stymied long-run economic development.by Melissa Dell.Ph.D

Méthodes d'apprentissage de la coordination multiagent : application au transport intelligent

Les problèmes de prise de décisions séquentielles multiagents sont difficiles à résoudre surtout lorsque les agents n'observent pas parfaitement l'état de Y environnement. Les approches existantes pour résoudre ces problèmes utilisent souvent des approximations de la fonction de valeur ou se basent sur la structure pour simplifier la résolution. Dans cette thèse, nous proposons d'approximer un problème de décisions séquentielles multiagent à observation limitée, modélisé par un processus décisionnel markovien décentralisé (DEC-MDP) en utilisant deux hypothèses sur la structure du problème. La première hypothèse porte sur la structure de comportement optimal et suppose qu'il est possible d'approximer la politique optimale d'un agent en connaissant seulement les actions optimales au niveau d'un petit nombre de situations auxquelles l'agent peut faire face dans son environnement. La seconde hypothèse porte, quant à elle, sur la structure organisationnelle des agents et suppose que plus les agents sont éloignés les uns des autres, moins ils ont besoin de se coordonner. Ces deux hypothèses nous amènent à proposer deux approches d'approximation. La première approche, nommée Supervised Policy Reinforcement Learning, combine l'apprentissage par renforcement et l'apprentissage supervisé pour généraliser la politique optimale d'un agent. La second approche se base, quant à elle, sur la structure organisationnelle des agents pour apprendre une politique multiagent dans des problèmes où l'observation est limitée. Pour cela, nous présentons un modèle, le D O F - D E C - M DP (Distance-Observable Factored Decentralized Markov Décision Process) qui définit une distance d'observation pour les agents. A partir de ce modèle, nous proposons des bornes sur le gain de récompense que permet l'augmentation de la distance d'observation. Les résultats empiriques obtenus sur des problèmes classiques d'apprentissage par renforcement monoagents et multiagents montrent que nos approches d'approximation sont capables d'apprendre des politiques proches de l'optimale. Enfin, nous avons testé nos approches sur un problème de coordination de véhicules en proposant une méthode de synchronisation d'agents via la communication dans un cadre à observation limitée