Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Price Rigidity and Strategic Uncertainty An Agent-based Approach

The phenomenon of infrequent price changes has troubled economists for decades. Intuitively one feels that for most price-setters there exists a range of inaction, i.e. a substantial measure of the states of the world, within which they do not wish to modify prevailing prices. However, basic economics tells us that when marginal costs change it is rational to change prices, too. Economists wishing to maintain rationality of price-setters resorted to fixed price adjustment costs as an explanation for price rigidity. In this paper we propose an alternative explanation, without recourse to any sort of physical adjustment cost, by putting strategic interaction into the center-stage of our analysis. Price-making is treated as a repeated oligopoly game. The traditional analysis of these games cannot pinpoint any equilibrium as a reasonable "solution" of the strategic situation. Thus there is genuine strategic uncertainty, a situation where decision-makers are uncertain of the strategies of other decision-makers. Hesitation may lead to inaction. To model this situation we follow the style of agent-based models, by modelling firms that change their pricing strategies following an evolutionary algorithm. Our results are promising. In addition to reproducing the known negative relationship between price rigidity and the level of general inflation, our model exhibits several features observed in real data. Moreover, most prices fall into the theoretical "range" without explicitly building this property into strategies.Agent-based modeling, Evolutionary algorithm, Price rigidity, Social learning, Strategic Uncertainty

Price Rigidity and Strategic Uncertainty An Agent-based Approach

Abstract. The phenomenon of infrequent price changes has troubled economists for decades. Intuitively one feels that for most price-setters there exists a range of inaction, i.e. a substantial measure of the states of the world, within which they do not wish to modify prevailing prices. However, basic economics tells us that when marginal costs change it is rational to change prices, too. Economists wishing to maintain rationality of price-setters resorted to fixed price adjustment costs as an explanation for price rigidity. In this paper we propose an alternative explanation, without recourse to any sort of physical adjustment cost, by putting strategic interaction into the center-stage of our analysis. Pricemaking is treated as a repeated oligopoly game. The traditional analysis of these games cannot pinpoint any equilibrium as a reasonable "solution" of the strategic situation. Thus there is genuine strategic uncertainty, a situation where decision-makers are uncertain of the strategies of other decision-makers. Hesitation may lead to inaction. To model this situation we follow the style of agent-based models, by modelling firms that change their pricing strategies following an evolutionary algorithm. Our results are promising. In addition to reproducing the known negative relationship between price rigidity and the level of general inflation, our model exhibits several features observed in real data. Moreover, most prices fall into the theoretical "range" without explicitly building this property into strategies

CO-EVOLUTIONARY BIDDING AND COOPERATION STRATEGIES FOR BUYERS IN POWER MARKETS

Master'sMASTER OF ENGINEERIN

Peak reduction in decentralised electricity systems : markets and prices for flexible planning

In contemporary societies, industrial processes as well as domestic activities rely to a large degree on a well-functioning electricity system. This reliance exists both structurally (the system should always be available) and economically (the prices for electricity affect the costs of operating a business and the costs of living). After many decades of stability in engineering principles and related economic paradigms, new developments require us to reconsider how electricity is distributed and paid for.Twowell-known examples of important technological developments in this regard are decentralised renewable energy generation (e.g. solar and wind power) and electric vehicles. They promise to be highly useful, for instance because they allow us to decrease our CO2 emissions and our dependence on energy imports. However, a widespread introduction of these (and related) technologies requires significant engineering efforts. In particular, two challenges to themanagement of electricity systems are of interest to the scope of this dissertation. First, the usage of these technologies has significant effects on howwell (part of) supply and demand can be planned ahead of time and balanced in real time. Planning and balancing are important activities in electricity distribution for keeping the number of peaks low (peaks can damage network hardware and lead to high prices). It can become more difficult to plan and balance in future electricity systems, because supply will partly depend on intermittent sunshine and wind patterns, and demand will partly depend on dynamic mobility patterns of electric vehicle drivers. Second, these technologies are often placed in the lower voltage (LV) tiers of the grid in a decentralised manner, as opposed to conventional energy sources, which are located in higher voltage (HV) tiers in central positions. This is introducing bi-directional power flows on the grid, and it significantly increases the number of actors in the electricity systems whose day-to-day decisionmaking about consumption and generation (e.g. electric vehicles supplying electricity back to the network) has significant impacts on the electricity system.In this dissertation, we look into dynamic pricing and markets in order to achieve allocations (of electricity and money) which are acceptable in future electricity systems. Dynamic pricing and markets are concepts that are highly useful to enable efficient allocations of goods between producers and consumers. Currently, they are being used to allocate electricity between wholesale traders. In recent years, the roles of the wholesale producer and the retailer have been unbundled in many countries of the world, which is often referred to as “market liberalisation”. This is supposed to increase competition and give end consumers more choice in contracts. Market liberalisation creates opportunities to design markets and dynamic pricing approaches that can tackle the aforementioned challenges in future electricity systems. However, they also introduce new challenges themselves, such as the acceptance of price fluctuations by consumers.The research objective of this dissertation is to develop market mechanisms and dynamic pricing strategies which can deal with the challenges mentioned above and achieve acceptable outcomes. To this end, we formulate three major research questions:First, can we design pricing mechanisms for electricity systems that support two necessary featureswell, which are not complementary—namely to encourage adaptations in electricity consumption and generation on short notice (by participants who have this flexibility), but also to enable planning ahead of electricity consumption and generation (for participants who can make use of planning)?Second, the smart grid vision (among others) posits that in future electricity systems, outcomeswill be jointly determined by a large number of (possibly) small actors and allocations will be mademore frequently than today. Which pricing mechanisms do not require high computational capabilities from the participants, limit the exposure of small participants to risk and are able to find allocations fast?Third, automated grid protection against peaks is a crucial innovation step for network operators, but a costly infrastructure program. Is it possible for smart devices to combine the objective of protecting network assets (e.g. cables) from overloading with applying buying and selling strategies in a dynamic pricing environment, such that the devices can earn back parts of their own costs?In order to answer the research questions, our methods are as follows: We consider four problems which are likely to occur in future electricity systems and are of relevance to our research objective. For each problem, we develop an agent-based model and propose a novel solution. Then, we evaluate our proposed solution using stochastic computational simulations in parameterised scenarios. We thus make the following four contributions:In Chapter 3,we design a market mechanism in which both binding commitments and optional reserve capacity are explicitly represented in the bid format, which can facilitate price finding and planning in future electricity systems (and therefore gives answers to our first research question). We also show that in this mechanism, flexible consumers are incentivised to offer reserve capacity ahead of time, whichwe prove for the case of perfect competition and showin simulations for the case of imperfect competition. We are able to show in a broad range of scenarios that our proposed mechanism has no economic drawbacks for participants. Furthermore (giving answers to our second research question), the mechanism requires less computational capabilities in order to participate in it than a contemporary wholesale electricitymarket with comparable features for planning ahead.In Chapter 4, we consider the complexity of dynamic pricing strategies that retailers could use in future electricity systems (this gives answers to our first, but foremost to our second research question). We argue that two important features of pricing strategies are not complementary—namely power peak reduction and comprehensibility of prices—and we propose indicators for the comprehensibility of a pricing strategy from the perspective of consumers. We thereby add a novel perspective for the design and evaluation of pricing strategies.In Chapter 5, we consider dynamic pricing mechanisms where the price is set by a single seller. In particular, we develop pricing strategies for a seller (a retailer) who commits to respect an upper limit on its unit prices (this gives answers to both our first and second research question). Upper price limits reduce exposure of market participants to price fluctuations. We show that employing the proposed dynamic pricing strategies reduces consumption peaks, although their parameters are being simultaneously optimised for themaximisation of retailer profits.In Chapter 6, we develop control algorithms for a small storage device which is connected to a low voltage cable. These algorithms can be used to reach decisions about when to charge and when to discharge the storage device, in order to protect the cable from overloading as well as to maximise revenue from buying and selling (this gives answers to our third research question). We are able to show in computational simulations that our proposed strategies perform well when compared to an approximated theoretical lower cost bound. We also demonstrate the positive effects of one of our proposed strategies in a laboratory setupwith real-world cable hardware.The results obtained in this dissertation advance the state of the art in designing pricing mechanisms and strategies which are useful for many use cases in future decentralised electricity systems. The contributions made can provide two positive effects: First, they are able to avoid or reduce unwanted extreme situations, often related to consumption or production peaks. Second, they are suitable for small actors who do not have much computation power but still need to participate in future electricity systems where fast decision making is needed

Spatial competition of learning agents in agricultural procurement markets

Spatially dispersed farmers supply raw milk as the primary input to a small number of large dairy-processing firms. The spatial competition of processing firms has short- to long-term repercussions on farm and processor structure, as it determines the regional demand for raw milk and the resulting raw milk price. A number of recent analytical and empirical contributions in the literature analyse the spatial price competition of processing firms in milk markets. Agent-based models (ABMs) serve by now as computational laboratories in many social science and interdisciplinary fields and are recently also introduced as bottom-up approaches to help understand market outcomes emerging from autonomously deciding and interacting agents. Despite ABMs' strengths, the inclusion of interactive learning by intelligent agents is not sufficiently matured. Although the literature of multi-agent systems (MASs) and multi-agent economic simulation are related fields of research they have progressed along separate paths. This thesis takes us through some basic steps involved in developing a theoretical basis for designing multi-agent learning in spatial economic ABMs. Each of the three main chapters of the thesis investigates a core issue for designing interactive learning systems with the overarching aim of better understanding the emergence of pricing behaviour in real, spatial agricultural markets. An important problem in the competitive spatial economics literature is the lack of a rigorous theoretical explanation for observed collusive behavior in oligopsonistic markets. The first main chapter theoretically derives how the incorporation of foresight in agents' pricing policy in spatial markets might move the system towards cooperative Nash equilibria. It is shown that a basic level of foresight invites competing firms to cease limitless price wars. Introducing the concept of an outside option into the agents' decisions within a dynamic pricing game reveals viihow decreasing returns for increasing strategic thinking correlates with the relevance of transportation costs. In the second main chapter, we introduce a new learning algorithm for rational agents using H-PHC (hierarchical policy hill climbing) in spatial markets. While MASs algorithms are typically just applicable to small problems, we show experimentally how a community of multiple rational agents is able to overcome the coordination problem in a variety of spatial (and non-spatial) market games of rich decision spaces with modest computational effort. The theoretical explanation of emerging price equilibria in spatial markets is much disputed in the literature. The majority of papers attribute the pricing behavior of processing firms (mill price and freight absorption) merely to the spatial structure of markets. Based on a computational approach with interactive learning agents in two-dimensional space, the third main chapter suggests that associating the extent of freight absorption just with the factor space can be ambiguous. In addition, the pricing behavior of agricultural processors – namely the ability to coordinate and achieve mutually beneficial outcomes - also depends on their ability to learn from each other

Annual Research Report, 2010-2011

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and government.https://digitalcommons.odu.edu/or_researchreports/1000/thumbnail.jp

Sustainable generation mix as a reference in effective design of electricity market structures and rules

EThOS - Electronic Theses Online ServiceGBUnited Kingdo