Contract Design for Energy Demand Response

Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. Current mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed. Extensive simulations show that compared to the current mechanism deployed in by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses.Comment: full version of paper accepted to IJCAI'1

Incentive Compatible Active Learning

We consider active learning under incentive compatibility constraints. The main application of our results is to economic experiments, in which a learner seeks to infer the parameters of a subject's preferences: for example their attitudes towards risk, or their beliefs over uncertain events. By cleverly adapting the experimental design, one can save on the time spent by subjects in the laboratory, or maximize the information obtained from each subject in a given laboratory session; but the resulting adaptive design raises complications due to incentive compatibility. A subject in the lab may answer questions strategically, and not truthfully, so as to steer subsequent questions in a profitable direction. We analyze two standard economic problems: inference of preferences over risk from multiple price lists, and belief elicitation in experiments on choice over uncertainty. In the first setting, we tune a simple and fast learning algorithm to retain certain incentive compatibility properties. In the second setting, we provide an incentive compatible learning algorithm based on scoring rules with query complexity that differs from obvious methods of achieving fast learning rates only by subpolynomial factors. Thus, for these areas of application, incentive compatibility may be achieved without paying a large sample complexity price.Comment: 22 page

A Groves-Like Mechanism in Risk Assessment

This paper links two research areas that have developed independently—incentives compatibility for public goods and elicitation of subjective probabilities. An analogy between incentives for reporting information in the two areas leads to the discovery of a new mechanism, based on the Groves mechanism, for eliciting subjective probabilities. In the public goods area, the analogy provides an extension of the basic theorem of truthful response to the more general case when one’s true valuation of the public good is state dependent. In the risk assessment area, the analogy provides a generalization of the traditional reporting mechanisms, proper scoring rules, and in doing so establishes a representation theorem for them. The paper considers three goals which a principal might have while choosing a transfer mechanism. These goals are: information pooling, strong research incentives for the agents, and identifiability of the agent with the best information. For two structures of information and the specific cases considered, the new mechanism performs well, compared with four traditional mechanisms, in achieving these goals

Crowdsourced Bayesian auctions

We investigate the problem of optimal mechanism design, where an auctioneer wants to sell a set of goods to buyers, in order to maximize revenue. In a Bayesian setting the buyers' valuations for the goods are drawn from a prior distribution D, which is often assumed to be known by the seller. In this work, we focus on cases where the seller has no knowledge at all, and "the buyers know each other better than the seller knows them". In our model, D is not necessarily common knowledge. Instead, each buyer individually knows a posterior distribution associated with D. Since the seller relies on the buyers' knowledge to help him set a price, we call these types of auctions crowdsourced Bayesian auctions. For this crowdsourced Bayesian model and many environments of interest, we show that, for arbitrary valuation distributions D (in particular, correlated ones), it is possible to design mechanisms matching to a significant extent the performance of the optimal dominant-strategy-truthful mechanisms where the seller knows D. To obtain our results, we use two techniques: (1) proper scoring rules to elicit information from the players; and (2) a reverse version of the classical Bulow-Klemperer inequality. The first lets us build mechanisms with a unique equilibrium and good revenue guarantees, even when the players' second and higher-order beliefs about each other are wrong. The second allows us to upper bound the revenue of an optimal mechanism with n players by an n/n--1 fraction of the revenue of the optimal mechanism with n -- 1 players. We believe that both techniques are new to Bayesian optimal auctions and of independent interest for future work.United States. Office of Naval Research (Grant number N00014-09-1-0597

Double Auctions in Markets for Multiple Kinds of Goods

Motivated by applications such as stock exchanges and spectrum auctions, there is a growing interest in mechanisms for arranging trade in two-sided markets. Existing mechanisms are either not truthful, or do not guarantee an asymptotically-optimal gain-from-trade, or rely on a prior on the traders' valuations, or operate in limited settings such as a single kind of good. We extend the random market-halving technique used in earlier works to markets with multiple kinds of goods, where traders have gross-substitute valuations. We present MIDA: a Multi Item-kind Double-Auction mechanism. It is prior-free, truthful, strongly-budget-balanced, and guarantees near-optimal gain from trade when market sizes of all goods grow to $\infty$ at a similar rate.Comment: Full version of IJCAI-18 paper, with 2 figures. Previous names: "MIDA: A Multi Item-type Double-Auction Mechanism", "A Random-Sampling Double-Auction Mechanism". 10 page

Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs

In the last decade, civic crowdfunding has proved to be effective in generating funds for the provision of public projects. However, the existing literature deals only with citizen's with positive valuation and symmetric belief towards the project's provision. In this work, we present novel mechanisms which break these two barriers, i.e., mechanisms which incorporate negative valuation and asymmetric belief, independently. For negative valuation, we present a methodology for converting existing mechanisms to mechanisms that incorporate agents with negative valuations. Particularly, we adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN mechanisms which incentivize strategic agents to contribute to the project based on their true preference. With respect to asymmetric belief, we propose a reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum mechanism. With BBR, we propose a general mechanism for civic crowdfunding which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards the project's provision contribute more than agents with lesser belief. Further, we also show that contributions are such that the project is provisioned at equilibrium.Comment: Accepted as full paper in IJCAI 201

Auctions, Bidding and Exchange Design

The different auction types are outlined using a classification framework along six dimensions. The economic properties that are desired in the design of auction mechanisms and the complexities that arise in their implementation are discussed. Some of the most interesting designs from the literature are analyzed in detail to establish known results and to identify the emerging research directions.Engineering and Applied Science

Mechanism design for information aggregation within the smart grid

The introduction of a smart electricity grid enables a greater amount of information exchange between consumers and their suppliers. This can be exploited by novel aggregation services to save money by more optimally purchasing electricity for those consumers. Now, if the aggregation service pays consumers for said information, then both parties could benefit. However, any such payment mechanism must be carefully designed to encourage the customers (say, home-owners) to exert effort in gathering information and then to truthfully report it to the aggregator. This work develops a model of the information aggregation problem where each home has an autonomous home agent, which acts on its behalf to gather information and report it to the aggregation agent. The aggregator has its own historical consumption information for each house under its service, so it can make an imprecise estimate of the future aggregate consumption of the houses for which it is responsible. However, it uses the information sent by the home agents in order to make a more precise estimate and, in return, gives each home agent a reward whose amount is determined by the payment mechanism in use by the aggregator. There are three desirable properties of a mechanism that this work considers: budget balance (the aggregator does not reward the agents more than it saves), incentive compatibility (agents are encouraged to report truthfully), and finally individual rationality (the payments to the home agents must outweigh their incurred costs). In this thesis, mechanism design is used to develop and analyse two mechanisms. The first, named the uniform mechanism, divides the savings made by the aggregator equally among the houses. This is both Nash incentive compatible, strongly budget balanced and individually rational. However, the agents' rewards are not fair insofar as each agent is rewarded equally irrespective of that agent's actual contribution to the savings. This results in a smaller incentive for agents to produce precise reports. Moreover, it encourages undesirable behaviour from agents who are able to make the loads placed upon the grid more volatile such that they are harder to predict. To resolve these issues, a novel scoring rule-based mechanism named sum of others' plus max is developed, which uses the spherical scoring rule to more fairly distribute rewards to agents based on the accuracy and precision of their individual reports. This mechanism is weakly budget balanced, dominant strategy incentive compatible and individually rational. Moreover, it encourages agents to make their loads less volatile, such that they are more predictable. This has obvious advantages to the electricity grid. For example, the amount of spinning reserve generation can be reduced, reducing the carbon output of the grid and the cost per unit of electricity. This work makes use of both theoretical and empirical analysis in order to evaluate the aforementioned mechanisms. Theoretical analysis is used in order to prove budget balance, individual rationality and incentive compatibility. However, theoretical evaluation of the equilibrium strategies within each of the mechanisms quickly becomes intractable. Consequently, empirical evaluation is used to further analyse the properties of the mechanisms. This analysis is first performed in an environment in which agents are able to manipulate their reports. However, further analysis is provided which shows the behaviour of the agents when they are able to make themselves harder to predict. Such a scenario has thus far not been discussed within mechanism design literature. Empirical analysis shows the sum of others' plus max mechanism to provide greater incentives for agents to make precise predictions. Furthermore, as a result of this, the aggregator increases its utility through implementing the sum of others' plus max mechanism over the uniform mechanism and over implementing no mechanism. Moreover, in settings which allow agents to manipulate the volatility of their loads, it is shown that the uniform mechanism causes the aggregator to lose utility in comparison to using no mechanism, whereas in comparison to no mechanism, the sum of others' plus max mechanism causes an increase in utility to the aggregator

Designing electricity transmission auctions

The UK has ambitious plans for exploiting offshore wind for electricity production in order to meet its challenging target under the EU Renewable Energy Directive. This could involve investing up to 20bn in transmission assets to bring electricity ashore. An investment of this magnitude calls for an efficient mechanism to determine which projects get financed and ensuring that only those projects that are selected can be delivered at least costs to consumers. The electricity regulatorﾒs ongoing tender auctions are likely to work well for point-to-point transmission and for networks already built. However, it is still unclear what kinds of models could be considered for complex meshed offshore (and onshore) networks where licences are granted not only to own and operate, but also to build a transmission network. This paper provides an extensive survey on the current theory and experience of auctions. The main objective is to discuss the design of auctions for transmission assets in which bidding for packages of transmission assets is a possibility