Negotiating Concurrently with Unknown Opponents in Complex, Real-Time Domains

We propose a novel strategy to enable autonomous agents to negotiate concurrently with multiple, unknown opponents in real-time, over complex multi-issue domains. We formalise our strategy as an optimisation problem, in which decisions are based on probabilistic information about the opponents' strategies acquired during negotiation. In doing so, we develop the first principled approach that enables the coordination of multiple, concurrent negotiation threads for practical negotiation settings. Furthermore, we validate our strategy using the agents and domains developed for the International Automated Negotiating Agents Competition (ANAC), and we benchmark our strategy against the state-of-the-art. We find that our approach significantly outperforms existing approaches, and this difference improves even further as the number of available negotiation opponents and the complexity of the negotiation domain increases

Power Load Management as a Computational Market

Power load management enables energy utilities to reduce peak loads and thereby save money. Due to the large number of different loads, power load management is a complicated optimization problem. We present a new decentralized approach to this problem by modeling direct load management as a computational market. Our simulation results demonstrate that our approach is very efficient with a superlinear rate of convergence to equilibrium and an excellent scalability, requiring few iterations even when the number of agents is in the order of one thousand. Aframework for analysis of this and similar problems is given which shows how nonlinear optimization and numerical mathematics can be exploited to characterize, compare, and tailor problem-solving strategies in market-oriented programming

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

A Secure and Fair Protocol that Addresses Weaknesses of the Nash Bargaining Solution in Nonlinear Negotiation

Negotiation with multiple interdependent issues is an important problem since much of real-world negotiation falls into this category. This paper examines the problem that, in such domains, agent utility functions are nonlinear, and thereby can create nonconvex Pareto frontiers. This in turn implies that the Nash Bargaining Solution, which has been viewed as the gold standard for identifying a unique optimal negotiation outcome, does not serve that role in nonlinear domains. In nonlinear domains, unlike linear ones, there can be multiple Nash Bargaining Solutions, and all can be sub-optimal with respect to social welfare and fairness. In this paper, we propose a novel negotiation protocol called SFMP (the Secure and Fair Mediator Protocol) that addresses this challenge, enabling secure multilateral negotiations with fair and pareto-optimal outcomes in nonlinear domains. The protocol works by (1) using nonlinear optimization, combined with a Multi-Party protocol, to find the Pareto front without revealing agent’s private utility information, and (2) selecting the agreement from the Pareto set that maximizes a fair division criterion we call approximated fairness. We demonstrate that SFMP is able to find agreements that maximize fairness and social welfare in nonlinear domains, and out-performs (in terms of outcomes and scalability) previously developed nonlinear negotiation protocols

General equilibrium with asymmetric information and default penalties

We introduce a two-period general equilibrium model with uncertainty and incomplete financial markets, where default is allowed and agents face in case they do default an utility penalty, which is their own private information. In this setting, if agents have heterogeneous characteristics they will generally pay different returns on any given asset, and thus the same promise made by different agents is in fact not equivalent. If asset trading is anonymous, then the same price is paid for promises whose value can be in fact quite different, and very severe adverse selection problems may arise as consequence. We thus incorporate in the above model an alternative way to negotiate the financial assets, under which an equilibrium exists and the adverse selection problem is mitigated. Succinctly, consumers trade assets non-anonymously with a set of financial intermediaires not allowed to default.Asymmetric information, adverse selection, default penalties, bilateral negotiation, equilibruim.

MACROECONOMIC ADJUSTMENT AND THE BALANCE OF BARGAINING POWER IN RURAL WEST AFRICAN HOUSEHOLDS

The example of cotton-producing households in Burkina Faso is used to investigate (1) how price-shifting macroeconomic adjustment policies affect the balance of bargaining power between spouses in West African households and (2) how the balance of power itself mediates the impact of policies on households' production, income, and welfare.Consumer/Household Economics, Crop Production/Industries,

Supply response of West African agricultural households

This paper explores the implications of preference heterogeneity between wives and husbands in nonresource-pooling rural West African households for the effect of crop price changes on agricultural production, i.e., their supply response. A "semi-cooperative" game-theoretic model of household decisionmaking, in which household members make unilateral time and income allocation decisions and negotiate over who controls these resources, is proposed. The model is used to show that Pareto efficiency in both production and consumption do not hold. It is then employed to simulate the supply response to cotton price increases accompanying agricultural sector liberalization in Burkina Faso in the early 1980s. The simulated semi-cooperative model predicts the cotton supply response of (monogamous) Burkinabé households to be 25 percent below that which would ensue in households facing the same production constraints yet whose members have identical preferences. The analysis indicates that in nonresource-pooling agricultural households, preference heterogeneity can be expected to mute supply response and may do so in a quantitatively significant manner. It illustrates how an intrahousehold approach that allows for such heterogeneity and for disaggregation of resource control by gender contributes to a better understanding of price effects.Gender ,Resource management. ,Households Decision making. ,Household resource allocation ,

Acceptance conditions in automated negotiation

In every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance condition

On-demand or Spot? Selling the cloud to risk-averse customers

In Amazon EC2, cloud resources are sold through a combination of an on-demand market, in which customers buy resources at a fixed price, and a spot market, in which customers bid for an uncertain supply of excess resources. Standard market environments suggest that an optimal design uses just one type of market. We show the prevalence of a dual market system can be explained by heterogeneous risk attitudes of customers. In our stylized model, we consider unit demand risk-averse bidders. We show the model admits a unique equilibrium, with higher revenue and higher welfare than using only spot markets. Furthermore, as risk aversion increases, the usage of the on-demand market increases. We conclude that risk attitudes are an important factor in cloud resource allocation and should be incorporated into models of cloud markets.Comment: Appeared at WINE 201

An out-of-equilibrium model of the distributions of wealth

The distribution of wealth among the members of a society is herein assumed to result from two fundamental mechanisms, trade and investment. An empirical distribution of wealth shows an abrupt change between the low-medium range, that may be fitted by a non-monotonic function with an exponential-like tail such as a Gamma distribution, and the high wealth range, that is well fitted by a Pareto or inverse power-law function. We demonstrate that an appropriate trade-investment model, depending on three adjustable parameters associated with the total wealth of a society, a social differentiation among agents, and economic volatility referred to as investment can successfully reproduce the distribution of empirical wealth data in the low, medium and high ranges. Finally, we provide an economic interpretation of the mechanisms in the model and, in particular, we discuss the difference between Classical and Neoclassical theories regarding the concepts of {\it value} and {\it price}. We consider the importance that out-of-equilibrium trade transactions, where the prices differ from values, have in real economic societies.Comment: 11 pages + 7 figures. in press on Quantitavive Financ