Asymmetry of Information within Family Networks

This paper studies asymmetry of information and transfers within 712 extended family networks from Tanzania. Using cross-reports on asset holdings, we construct measures of mis-perception of living standards among households within the same network. We contrast altruism, pressure, exchange and risk sharing as motives to transfer in simple models with asymmetric information. Testing the model predictions in the data uncovers the active role played by recipients of transfers. Our findings suggest that recipients set the terms of the transfers, either by exerting pressure on donors or because they hold substantial bargaining power in their exchange relationships

Asymmetry of Information within Family Networks

This paper studies asymmetry of information and transfers within a unique data set of 712 extended family networks from Tanzania. Using cross-reports on asset holdings, we construct measures of misperception of income among all pairs of households belonging to the same network. We show that there is significant asymmetry of information and no evidence of major systematic over-evaluation or under-evaluation of income in our data, although there is a slight over-evaluation on the part of migrants regarding non-migrants. We develop a static model of asymmetric information that contrasts altruism, pressure and exchange as motives to transfer. The model makes predictions about the correlations between misperceptions and transfers under these competing explanations. Testing these predictions in the data uncovers the active role played by the recipient. Our findings suggest that the recipient sets the terms of the transfers, either by exerting pressure to give on the donor or by holding the bargaining power during the exchange of services with the donor

The 2016 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2016 (Power TAC 2016). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many o

The 2017 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2017 (Power TAC 2017). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate ut

The 2020 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2020 (Power TAC 2020). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, for rectifying supply-demand imbalances, for contributions to peak demand, and for customer connections. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate the effects of locational-marginal pricing through manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of whom have production capacity suc

The 2013 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2013 (Power TAC 2013). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins