Community Structure and Market Outcomes: A Repeated Games in Networks Approach

Consider a large market with asymmetric information, in which sellers choose whether to cooperate or deviate and ?cheat?their buyers, and buyers decide whether to re-purchase from di¤erent sellers. We model active trade relationships as links in a buyer-seller network and suggest a framework for studying repeated games in such networks. In our framework, buyers and sellers have rich yet incomplete knowledge of the network structure; allowing us to derive meaningful conditions that determine whether a network is consistent with trade and cooperation between every buyer and seller that are connected. We show that three network features reduce the minimal discount factor necessary for sustaining cooperation: moderate competition, sparseness, and segregation. We ? nd that the incentive constraints rule out networks that maximize the volume of trade and that the constrained trade maximizing networks are in between ?old world? segregated and sparse networks, and a ?global market?Buyer-Seller networks; repeated games; moral hazard;asymmetric information; trust; cooperation; institutions

Information Markets and Nonmarkets

As large amounts of data become available and can be communicated more easily and processed more e¤ectively, information has come to play a central role for economic activity and welfare in our age. This essay overviews contributions to the industrial organization of information markets and nonmarkets, while attempting to maintain a balance between foundational frameworks and more recent developments. We start by reviewing mechanism-design approaches to modeling the trade of information. We then cover ratings, predictions, and recommender systems. We turn to forecasting contests, prediction markets, and other institutions designed for collecting and aggregating information from decentralized participants. Finally, we discuss science as a prototypical information nonmarket with participants who interact in a non-anonymous way to produce and disseminate information. We aim to make the reader familiar with the central notions and insights in this burgeoning literature and also point to some open critical questions that future research will have to address

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention

Recommendation Systems: Decision Support for the Information Economy

Information Systems Working Papers Serie

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

This work was supported by the contract OTRI-4408 between the University of Granada and the Royal Academy of Engineering of Spain financed by Ferrovial S.A. Eugenio Martinez Camara was supported by the Spanish Government fellowship programme Juan de la Cierva Incorporacion (IJC2018-036092-I).The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.University of Granada - Ferrovial S.A. OTRI-4408Royal Academy of Engineering of Spain - Ferrovial S.A. OTRI-4408Spanish Government fellowship programme Juan de la Cierva Incorporacion IJC2018-036092-