Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff

In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy

Model Selection in an Information Economy : Choosing what to Learn

As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance. In contrast to traditional completeinformation economic scenarios, agents that are operating in an electronic marketplace often do so under considerable uncertainty. In order to reduce their uncertainty, these agents must learn about the world around them. When an agent producer is engaged in a learning task in which data collection is costly, such as learning the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the agent has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule. In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one-shot decision and show that moderate complexity schedules, in particular two-part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision-theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period. By explicitly considering the both the learnability and the profit extracted by different price schedules, a producer can extract more profit as it learns than if it naively chose models that are accurate once learned.Online learning; information economics; model selection; direct search

LA COMPUTACIÓN COGNOSCITIVA – EL MUNDO DE LA CONCIENCIA

Cognitive computer science has been presented as a transdisciplinary investigation of cognitive and information science that investigates the processing and internal information mechanism along with brain processes and natural intelligence and its engineering applications. In this work we attempt to provide an enlightening perspective on the past, present and future of cognitive computing, analysing the development of computer science from the classical information theory, contemporary computing, and cognitive computing which is a deep interdisciplinary research that addresses the problems from modern common root computing, computing, software engineering, such as the future generation computer architecture known as cognitive computers of human memory. The goal of autonomous computing is to create computing systems capable of managing to a much greater extent than the current one. This paper presents Unity, a decentralized architecture for autonomous computing based on multiple interacting agents called autonomous elements. We illustrate how the unit architecture performs various desired behaviours of the autonomous system, including goal-oriented self-healing, self-healing, and real-time self-optimization. These elements are important for your comprehension and treatment in the process of computer engineer in, as well as in the area of Electronics and Telecommunications.  La informática cognitiva se ha presentado como una investigación transdisciplinaria de la ciencia cognitiva y de la información que investiga el mecanismo de procesamiento e información interna junto con los procesos cerebrales y la inteligencia natural y sus aplicaciones de ingeniería. En este trabajo intentamos proporcionar una perspectiva esclarecedora sobre el pasado, el presente y el futuro de la informática cognitiva, analizando el desarrollo de la informática a partir de la teoría de la información clásica, la informática contemporánea y la informática cognitiva, que es una investigación interdisciplinaria profunda que aborda los problemas desde informática raíz común moderna, informática, ingeniería de software, como la arquitectura informática de la generación futura conocida como computadoras cognitivas de la memoria humana. El objetivo de la informática autónoma es crear sistemas informáticos capaces de gestionar en mayor medida que el actual. Este artículo presenta Unity, una arquitectura descentralizada para computación autónoma basada en múltiples agentes interactivos llamados elementos autónomos. Ilustramos cómo la arquitectura de la unidad realiza varios comportamientos deseados del sistema autónomo, incluida la autocuración orientada a objetivos, la autocuración y la autooptimización en tiempo real. Estos elementos son importantes para su comprensión y tratamiento en el proceso de ingeniería informática, así como en el área de Electrónica y Telecomunicaciones

Model Selection in an Information Economy: Choosing what to Learn

In an economy in which a producer must learn the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the producer has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule. In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one-shot decision and show that moderate complexity schedules, in particular two-part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision-theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period.http://deepblue.lib.umich.edu/bitstream/2027.42/50438/1/comp-intel.pd

Mining Revenue-Maximizing Bundling Configuration

With greater prevalence of social media, there is an increas-ing amount of user-generated data revealing consumer pref-erences for various products and services. Businesses seek to harness this wealth of data to improve their marketing strategies. Bundling, or selling two or more items for one price is a highly-practiced marketing strategy. In this pa-per, we address the bundle configuration problem from the data-driven perspective. Given a set of items in a seller’s in-ventory, we seek to determine which items should belong to which bundle so as to maximize the total revenue, by mining consumer preferences data. We show that this problem is NP-hard when bundles are allowed to contain more than two items. Therefore, we describe an optimal solution for bundle sizes up to two items, and propose two heuristic solutions for bundles of any larger size. We investigate the effective-ness and the efficiency of the proposed algorithms through experimentations on real-life rating-based preferences data

Pricing Information Bundles in a Dynamic Environment

We explore a scenario in which a monopolist producer of information goods seeks to maximize its profits in a market where consumer demand shifts frequently and unpredictably. The producer is free to set an arbitrarily complex price schedule-a function that maps the set of purchased items to a price-but without direct knowledge of consumer demand it cannot compute the optimal schedule. Instead, it must employ a form of optimization based on trial and error. By means of a simple model of consumer demand and a modified version of a simple nonlinear optimization routine, we study a variety of parameterizations of the price schedule and quantity some of the relationships among learnability, complexity, and profitability. In particular, we show that fixed pricing or simple two-parameter dynamic pricing schedules are preferred when consumer demand shifts frequently, but that dynamic pricing based on more complex schedules tends to be most profitable when consumer demand shifts very infrequently.http://deepblue.lib.umich.edu/bitstream/2027.42/50440/1/DynamicBundling.pd