Signalling and obfuscation for congestion control

We aim to reduce the social cost of congestion in many smart city applications. In our model of congestion, agents interact over limited resources after receiving signals from a central agent that observes the state of congestion in real time. Under natural models of agent populations, we develop new signalling schemes and show that by introducing a non-trivial amount of uncertainty in the signals, we reduce the social cost of congestion, i.e., improve social welfare. The signalling schemes are efficient in terms of both communication and computation, and are consistent with past observations of the congestion. Moreover, the resulting population dynamics converge under reasonable assumptions.     

Competitive Pricing Strategies for Software and SaaS Products

We study the pricing strategies for a software firm and an entrant software-as-a-service (SaaS) firm in two customer markets: the market composed of the incumbent’s past customers and the market of new customers. We build a game theoretical model to investigate how user costs and the quality differential between products affect firms’ pricing in different customer markets. Our findings show that it is not always optimal for the software firm to price discriminate between its old users and the new customers. The entrant firm would be better off acquiring only the new customers when the SaaS quality is sufficiently low.     

固定费率下城市出租车拥挤收费模型与算法

针对出租车自身的特殊性, 研究了实施拥挤收费政策过程中出租车是否也应进行拥挤收费。在收费费率已知条件下, 采用组合网络均衡模型描述了出租车拥挤收费前后对出行产生、模式划分、流量分配以及出行分布的影响, 构建了与之等价的最优化模型, 设计了求解模型的启发式算法, 比较了对出租车征收拥挤费前后的社会福利变化。算例表明, 收费前的社会福利总是明显大于收费后的, 并且二者之间的差距随着出行者时间价值(VOT)的增加而减小, 随着弹性灵敏参数的增加而增大。这一研究有助于为拥挤收费政策制定者提供科学的决策依据。     

Modeling the consumer benefit in the optimal power flow

The addition of a model of the consumer into the traditional optimal power flow (OPF) algorithm that minimizes supplier costs is investigated. The development of such a model is based on the solution of the OPF using an objective function for maximization of social welfare. A traditional OPF algorithm can be modified to solve the social welfare maximization problem by including price-dependent load models. This modification to the traditional OPF is intuitive and very simple. This modified OPF formulation facilitates simulation of spot markets for both real and reactive power. The algorithm is effective on systems of hundreds of buses, but small examples to compare the results to the traditional OPF are also insightful. The impact of price-dependent loads on systems with transmission congestion, increased fuel costs, and voltage problems can be studied.     

Electric Power Network Oligopoly as a Dynamic Stackelberg Game

Over the last two decades, the electricity industry has shifted from regulation of monopolistic and centralized utilities towards deregulation and promoted competition. With increased competition in electric power markets, system operators are recognizing their pivotal role in ensuring the efficient operation of the electric grid and the maximization of social welfare. In this article, we propose a hypothetical new market of dynamic spatial network equilibrium among consumers, system operators and electricity generators as solution of a dynamic Stackelberg game. In that game, generators form an oligopoly and act as Cournot-Nash competitors who non-cooperatively maximize their own profits. The market monitor attempts to increase social welfare by intelligently employing equilibrium congestion pricing anticipating the actions of generators. The market monitor influences the generators by charging network access fees that influence power flows towards a perfectly competitive scenario. Our approach anticipates uncompetitive behavior and minimizes the impacts upon society. The resulting game is modeled as a Mathematical Program with Equilibrium Constraints (MPEC). We present an illustrative example as well as a stylized 15-node network of the Western European electric grid.     

Bundling strategy in base-supplemental goods markets: the case of Microsoft

We show that bundling is the optimal pricing strategy for a base good monopolist who also supplies a supplemental good under zero marginal cost of product. Without the exit of the rival firm, bundling is a profitable strategy because it increases the profits in the base good market. We show that bundling lowers social welfare as well as rival firms' profit if the supplemental goods are close substitutes. Otherwise, bundling may actually generate welfare enhancements. Our analysis applies directly to the computer software markets and the case of Microsoft.     

Market power and welfare effects in DC power flow electricity models with thermal line losses

A nodal electric power network with Cournot–Nash interaction among power generators is formulated as a mixed complementarity problem. The model incorporates a direct current (DC) power flow approximation with thermal line losses to model real-time flows. We include constant wheeling rate and variable congestion charges for transmission of electricity. Market power and welfare effects are measured in an aggregated Indiana electric grid model. We find that imposing DC power flow constraints in a model results in significant changes in social welfare estimates. Line losses are also an important factor affecting market power and welfare of market participants in the case study.     

R&;D for Quality Improvement and Network Externalities

We investigate the bearings of network externalities on product quality improvements requiring costly R&D investments. The model considers the dynamic behaviour of a monopolist alternatively maximising profits or social welfare. On the one hand, we confirm much of the acquired wisdom from the static literature on the same topic, about the arising of quality undersupply at the private optimum. On the other, we show that the monopoly optimum requires specific viability conditions, while the social optimum is always viable. We also show that the presence of network externalities affects the optimal investment behaviour of the profit-seeking firm but not that of a benevolent planner, who serves all consumers from the outset.     

具有网络外部效应的三度价格歧视研究

通过建立两厂商和两子市场的两阶段博弈模型,分析具有网络外部效应的寡头竞争市场厂商实施三度歧视定价的产出、价格和社会福利问题,研究结果表明:厂商通过歧视定价提高了强市场的价格,相应降低了弱市场的价格,虽然总产出没有改变,但却减少了社会总福利.     

Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design

This article presents an agent-based integrated model of a real, financial, and monetary economy. The model is characterized by a monopolist firm that supplies a single homogeneous product in the goods market, hires workers in the labor market, and demands loans in the credit market; a trade union that sets the nominal wage; N heterogeneous households that buy the consumption good, provide the labor force, and trade the firm’s equity in the stock market; and a bank that lends money to the firm at an interest rate set according to a monetary policy strategy. The model is used to perform monetary policy experiments. A monetary policy rule which targets the gap between the current output and the potential output in the full employment case is investigated, studying the effects on the economy for different degrees of policy tightness. The monetary policy rule is compared to a random policy rule that conserves a similar structure. Results show that a tight monetary policy clearly over performs the random policy rule. Moreover, results corroborate the effectiveness of monetary policy in limiting inflation and increasing welfare.     

Network Expansion to Mitigate Market Power

Constrained transmission capacity in electricity networks may give generators the possibility to game the market by specifically causing congestion and thereby appropriating excessive rents. Investment in network capacity can ameliorate such behavior by reducing the potential for strategic behavior. However, modeling Nash equilibria between generators, which explicitly account for their impact on the network, is mathematically and computationally challenging. We propose a three-stage model to describe how network investment can reduce market power exertion: a benevolent planner decides on network upgrades for existing lines anticipating the gaming opportunities by strategic generators. These firms, in turn, anticipate their impact on market-clearing prices and grid congestion. In this respect, we provide the first model endogenizing the trade-off between the costs of grid investment and benefits from reduced market power potential in short-run market clearing. In a numerical example using a three-node network, we illustrate three distinct effects: firstly, by reducing market power exertion, network expansion can yield welfare gains beyond pure efficiency increases. Anticipating gaming possibilities when planning network expansion can push welfare close to a first-best competitive benchmark. Secondly, network upgrades entail a relative shift of rents from producers to consumers when congestion rents were excessive. Thirdly, investment may yield suboptimal or even disequilibrium outcomes when strategic behavior of certain market participants is neglected in network planning.     

Exploration costs as a means for improving performance in multiagent systems

We consider settings were agents are faced with several possible opportunities and need to choose one. Each opportunity may offer a different utility to the agent, and determining this utility may consume resources. The underlying costly exploration problem is termed “economic search”, though its essence is different from the traditional search notion in artificial intelligence (e.g. BFS, IDDFA, and A*), as there is no underlying combinatorial structure to the opportunities. We study the effects that search costs can have on individual and aggregate utility in distributed multi-agent economic-search settings. Traditionally, in such setting, search costs are regarded as a market inefficiency, and, as such, as something to be avoided or reduced to a minimum. We show, in contrast, that in many search settings, the introduction of search costs can actually improve the aggregate social welfare, or even the expected utility of each and every individual agent.We note that the proceeds from the search costs are assumed to be wasted, with no one directly benefiting from them. We demonstrate the benefits of search costs in both one-sided and two-sided search settings, using standard, classical models from economic-search theory. For the designers of multiagent systems, the results imply that deliberate (and potentially artificial) increase of search costs should be considered as possible means to improving the system’s overall performance.     

Agent-based approach to analyzing the effects of dynamic ridesharing in a multimodal network

An agent-based modeling for dynamic ridesharing in a multimodal network is proposed in this paper. The study aims to evaluate the performance of dynamic ridesharing system within a multimodal network and explore the competing mechanism between dynamic ridesharing and public transit, with the presence of managed lane facility. The modeling process simulates the interaction between travelers and the network, and applies a heuristic algorithm to model travelers' decision making process under uncertainty. The model is applicable to networks with varying demographics. Multiple scenarios based on the classic Sioux Falls network have been examined. The modeling results demonstrate that the effects of dynamic ridesharing on a network differ with traffic demand and market penetrations of various travel modes. In networks with high travel demand and low market penetration of public transit, the benefits of dynamic ridesharing system on reducing congestion and providing reliable travel time are quite limited. To enhance the effectiveness of dynamic ridesharing, traffic operators may consider project investments on managed lane facilities. In networks with high market penetration of public transit, dynamic ridesharing may attract large amounts of short distance trips and aggravate congestion, especially at the initial launching phase. Policy makers would want to ensure that the existing infrastructure is sufficient to accommodate the extra traffic induced by ridesharing. Ridesharing service providers might also consider proper strategies to avoid “abuse” of the system by short trips and accelerate the market penetration.     

Cloud Computing System Management Under Flat Rate Pricing

Success of cloud computing service depends on an acceptable pricing mechanism both by users and the service provider. Piece rate pricing by counting work load should be favorable for the service provider due to the QoS control and finite resource, such as computing and, communication powers. Though the pricing mechanism based on counting work load is reasonable and fair, the experiences learned from ADSL, 3G and Wi-Fi show a different story. The flat rate pricing mechanism is the winner all the way. This study proposes a flat rate pricing mechanism with congestion control, called FRPCC. In the cloud computing system, allocation of resources can be formulated as an optimization problem seeking to maximize the sum of the utility function of each user under the constraints of fairness. The piece rate pricing mechanism is easy to achieve the social welfare but is not easy to be acceptable for customers. Consequently, we propose a congestion control scheme to reach the same goal with a flat rate pricing mechanism. The proposed FRPCC approach can achieve social welfare in the cloud computing environment. Performance evaluations show efficacy of, FRPCC approach in providing social welfare under fairness and preventing congestion.     

Adverse Weather and Commuting Speed

This paper investigates the welfare effect of travelling through congested areas and adverse weather through changes in the speed of individuals’ car trips based on the entire commuting trip. Weather measurements are local and time specific (hourly basis). As most commuters travel twice a day between home and work, we are able to employ panel data techniques, which deals with issues related to unobserved heterogeneity and data selection. We find that travelling through congested areas reduces speed by about 7%. For most commuters the welfare effects of adverse weather conditions are negative but small. However, the commuters’ welfare costs due to rain are rather substantial during the evening peak in congested areas (and up to 12% of the overall commuting costs).     

Self-Fulfilling Signal of an Endogenous State in Network Congestion Games

We consider the problem of coordination via signaling in network congestion games to improve social welfare deteriorated by incomplete information about traffic flow. Traditional studies on signaling, which focus on exogenous factors of congestion and ignore congestion externalities, fail to discuss the oscillations of traffic flow. To address this gap, we formulate a problem of designing a coordination signal on endogenous information about traffic flow and introduce a self-fulfilling characteristic of a signal that guarantees an outcome flow consistent with the signal itself without causing the unwanted oscillation. An instance of the self-fulfilling signal is shown in the case of a Gaussian signal distribution. In addition, we show simple numerical examples. The results reveal how a self-fulfilling signal suppresses the oscillation and simultaneously improves social welfare through improved network efficiency.     

Multiagent resource allocation with sharable items

We study a particular multiagent resource allocation problem with indivisible, but sharable resources. In our model, the utility of an agent for using a bundle of resources is the difference between the value the agent would assign to that bundle in isolation and a congestion cost that depends on the number of agents she has to share each of the resources in her bundle with. The valuation function determining the value and the delay function determining the congestion cost can be agent-dependent. When the agents that share a resource also share control over that resource, then the current users of a resource will require some compensation when a new agent wants to join them using the resource. For this scenario of shared control, we study the existence of distributed negotiation protocols that lead to a social optimum. Depending on constraints on the valuation functions (mainly modularity), on the delay functions (such as convexity), and on the structural complexity of the deals between agents, we prove either the existence of a sequences of deals leading to a social optimum or the convergence of all sequences of deals to such an optimum. We also analyse the length of the path leading to such optimal allocations. For scenarios where the agents using a resource do not have shared control over that resource (i.e., where any agent can use any resource she wants), we study the existence of pure Nash equilibria, i.e., allocations in which no single agent has an incentive to add or drop any of the resources she is currently holding. We provide results for modular valuation functions, we analyse the length of the paths leading to a pure Nash equilibrium, and we relate our findings to results from the literature on congestion games.     

A Large-Scale Spatial Optimization Model of the European Electricity Market

In this paper, we present a large-scale spatial model of the European electricity market including both generation and the physical transmission network (DC Load Flow approach). The model was developed to analyze various questions on market design, congestion management, and investment decisions, with a focus on Germany and Continental Europe. It is a bottom-up model combining electrical engineering and economics: its objective function is welfare maximization, subject to line flow, energy balance, and generation constraints. The model provides simulations on an hourly basis, taking into account variable demand, wind input, unit commitment, start-up costs, pump storage, and other details. Various forms of spatial price discrimination can be implemented, such as locational marginal pricing (“nodal pricing”), or zonal pricing. With over 2,000 nodes and over 3,000 lines, this is one of the largest models developed to date, and allows a highly differentiated spatial analysis. We report modeling results regarding efficient congestion management for Germany and Europe, optimal network expansion under the aspect of increased wind energy production, and the impact of network constraints on location decisions of generation investments.     

SOCIAL GAMING AND CONTROL BY DISLOCATIONS

Social gaming and control can be imposed by powerful political agents through impact dislocations at discrete points in time. Each dislocation represents a social or economic behaviour modification with a concomitant new subsequent socio-economic dynamics until the next dislocation. The over-all discrete sequence of dislocations experienced by a society, such as an interregional system of economic development, is generated by all the impacts imposed by all the agents of power as they game with each other and attempt to sway the over-all socio-economic process in a direction favorable to the particular goals and objectives perceived by each individual agent of power. Particular attention is given to the role of such social gaming and control in a hierarchical structure of an interacting interregional system of economic development.