A Contract for Demand Response based on Probability of Call
|
|
- Karin Joseph
- 5 years ago
- Views:
Transcription
1 A Contract for Demand Response based on Probability of Call Jose Vuelvas, Fredy Ruiz and Giambattista Gruosso Pontificia Universidad Javeriana - Politecnico di Milano
2 2 Outline Introduction Problem Setting Contract and consumer s problem Consumer optimal behavior Numerical case study Conclusions
3 Net consumption per hour 3 Introduction: drawbacks of baseline estimation Traditional incentive-based DR programs require an estimated baseline against which consumer s load reduction is measured. The current methods for establishing baseline: i) averaging techniques, ii) regression approaches, etc. Incentive payment = (Reduction) x (Reward/kWh) Reduction = Baseline - Observed baseline Reduction Actual consumption Peak event Hours of day
4 Net consumption per hour Introduction: drawbacks of baseline estimation Traditional incentive-based DR programs require an estimated baseline against which consumer s load reduction is measured. The current methods for establishing baseline: i) averaging techniques, ii) regression approaches, etc. Incentive payment = (Reduction) x (Reward/kWh) Reduction = Baseline - Observed baseline Reduction Actual consumption Peak event Hours of day Problem: Baseline manipulation (Severin Borenstein, 2014; Vuelvas and Ruiz, 2017; Chao, 2011). 3
5 4 Our approach A new contract based on the probability of call is proposed.
6 4 Our approach A new contract based on the probability of call is proposed. Probability of call: the chance of a consumer to be selected by the aggregator to serve as DR resource at a given period.
7 4 Our approach A new contract based on the probability of call is proposed. Probability of call: the chance of a consumer to be selected by the aggregator to serve as DR resource at a given period. Consumer self reports his baseline (it is not estimated).
8 4 Our approach A new contract based on the probability of call is proposed. Probability of call: the chance of a consumer to be selected by the aggregator to serve as DR resource at a given period. Consumer self reports his baseline (it is not estimated). Agents bid information in terms of energy (baseline and reduction capacity).
9 Our approach A new contract based on the probability of call is proposed. Probability of call: the chance of a consumer to be selected by the aggregator to serve as DR resource at a given period. Consumer self reports his baseline (it is not estimated). Agents bid information in terms of energy (baseline and reduction capacity). In this model, the main objective of the aggregator is to select randomly which participant consumers are called to perform DR. Generators SO DR aggregator Contracts User 1 User 2 User I 4
10 G(qi; bi) 5 Consumer model Utility function: G(q i (consumption) ; b i (baseline)) Customer satisfaction function. γ i 2 q2 i + [γ ib i + p]q i Parameters: p 2 2γ i + γ ib 2 i 2 + pb i γ i : marginal utility (energy preference) p: energy price b i : baseline Variable: q i : actual consumption q i b i b i + p γ i
11 Ui(qi; bi) Problem Setting The energy price p is given. The energy total cost is π i (q i ) = pq i The payoff function without DR is defined as U i (q i ; b i ) = G(q i ; b i ) π i (q i ) b i is the rational decision (optimal solution) of U i (q i ; b i ) q i b i 6
12 Ui(qi; bi) Problem Setting The energy price p is given. The energy total cost is π i (q i ) = pq i The payoff function without DR is defined as U i (q i ; b i ) = G(q i ; b i ) π i (q i ) b i is the rational decision (optimal solution) of U i (q i ; b i ) q i b i Let p 2 be the rebate price. E.g. the incentive could be p 2 ( ˆb i q i ) +. The superscript ˆ means declared information. ˆq i is reported energy consumption under DR. r i is a binary variable that indicates if user i is called to participate in DR. 6
13 7 Contract and consumer s problem (1/2) 1. Aggregator announces p and p 2 (price and incentive)
14 7 Contract and consumer s problem (1/2) 1. Aggregator announces p and p 2 (price and incentive) 2. Consumers report ˆq i and ˆb i (information declaration)
15 7 Contract and consumer s problem (1/2) 1. Aggregator announces p and p 2 (price and incentive) 2. Consumers report ˆq i and ˆb i (information declaration) 3. Aggregator defines r i (random selection)
16 7 Contract and consumer s problem (1/2) 1. Aggregator announces p and p 2 (price and incentive) 2. Consumers report ˆq i and ˆb i (information declaration) 3. Aggregator defines r i (random selection) 4. Consumers decide q i (demand response event) Questions: What is the declared information that maximizes the consumer benefit? What is the uncertainty that user faces?
17 8 Contract and consumer s problem (2/2) Let π r i i,3 ( ˆb i, ˆq i, q i ) be the new aggregator payment scheme: π ri i,3 ( ˆb i, ˆq i, q i ) = (non-called) r i = 0 p max( ˆb i, q i ) buy the baseline or pay the consumption r i = 1 (called) pq i p 2 ( ˆb i q i ) + + p 2 q i ˆq i energy cost incentive payment deviation penalty
18 8 Contract and consumer s problem (2/2) Let π r i i,3 ( ˆb i, ˆq i, q i ) be the new aggregator payment scheme: π ri i,3 ( ˆb i, ˆq i, q i ) = (non-called) r i = 0 p max( ˆb i, q i ) buy the baseline or pay the consumption r i = 1 (called) pq i p 2 ( ˆb i q i ) + + p 2 q i ˆq i energy cost incentive payment deviation penalty The optimization problem: [ ˆb i, ˆqi, q i ] = arg max ˆb i, ˆq i,q i {0,q max,i } J i = E(G(q i ; b i ) π r i i,3 ( ˆb i, ˆq i, q i ))
19 Contract and consumer s problem (2/2) Let π r i i,3 ( ˆb i, ˆq i, q i ) be the new aggregator payment scheme: π ri i,3 ( ˆb i, ˆq i, q i ) = (non-called) r i = 0 p max( ˆb i, q i ) buy the baseline or pay the consumption r i = 1 (called) pq i p 2 ( ˆb i q i ) + + p 2 q i ˆq i energy cost incentive payment deviation penalty The optimization problem: [ ˆb i, ˆqi, q i ] = arg max ˆb i, ˆq i,q i {0,q max,i } J i = E(G(q i ; b i ) π r i i,3 ( ˆb i, ˆq i, q i )) The contract is settled as a two-stage procedure: Stage 1) Given the prices p and p 2, each consumer reports ˆb i and ˆq i to aggregator. Stage 2) Aggregator determines which users are called by means of the variable r i. Each agent decides his actual energy consumption q i. 8
20 9 Consumer optimal behavior When not called for the DR event, the optimal user response, given his previous declared information, is described by the following theorem: Theorem The optimal consumption qi,r i for the signal r i = 0 of a participant consumer in the proposed contract is: b i 0 ˆb i b i strategy A qi,r i =0 = ˆb i b i < ˆb i b i + p/γ i strategy B b i + p/γ i b i + p/γ i < ˆb i q max,i strategy C
21 10 Consumer optimal behavior When called for the DR event, the optimal user response, given his previous declared information, is described by the following theorem: Theorem The optimal consumption qi,r i for the signal r i = 1 of a participant consumer in the proposed contract is: qi,r i =1 = b i b i ˆb i q max,i, b i ˆq i ˆb i q max,i strategy U ˆq i b i p 2 γ i ˆb i q max,i, b i 2p 2 γ i ˆq i ˆb i b i strategy V ˆq i α ˆb i b i p 2 γ i, b i 2p 2 γ i ˆq i b i p 2 γ i (b i p 2 γ i ) + b i 2p 2 γ i ˆb i α, b i 2p 2 γ i ˆq i ˆb i b i p 2 γ i (b i p 2 γ i ) + 0 ˆb i b i 3p 2 2γ i, 0 ˆq i ˆb i b i 2p 2 γ i (b i 2p 2 γ i ) + b i 3p 2 2γ i ˆb i q max,i, 0 ˆq i b i 2p 2 γ i with α = γ i b 2 i 2p 2 γ i b i ˆq i p 2 b i + γ i ˆq i 2 2p ˆq i + p 2 2γ i. strategy W strategy Y strategy Z strategy X
22 11 Consumer optimal behavior Knowing the optimal consumer responses to the random signal r i, the best strategy in the first stage is to report the baseline and reduction level of the following theorem: Theorem Given qi,r i from Theorems 1 and 2, then the optimal reports ˆb i and ˆqi are: { pri p 2 ˆb i = γ i (1 p ri ) + b i 0 p ri p p 2 +p p q max,i p 2 +p p r i 1 ( ˆq i = b i p ) 2 γ i +
23 12 Consumer optimal behavior A user decides to participate in the program if his profit (net benefit) is greater, or at least equal, to what he gets when not participating. From the previous theorems, the expected profit of the consumer is: Collorary The optimal expected profit J is: bi 2γ i 2 + pr i p2 2 2γ i (1 p ri ) 0 p ri p p 2 +p Ji = p 2 2γ i + b i p pq max,i + b2 i γ i 2 p2 p ri 2γ i + p2 2 pr i 2γ i p b i p ri b i p 2 p ri + pq max,i p ri + p 2 q max,i p ri p 2 +p p r i 1
24 13 Contract properties Individually rational (voluntary participation): a user that participates in this approach obtains a profit at least as good as he does not signing the DR contract. Incentive compatibility on the reported energy consumption under DR: a consumer informs the truthful consumption under DR according to his preferences. Asymptotic incentive compatibility on the reported baseline: as the probability of call tends to zero, the consumer s optimal strategy is to declare ˆb i = b i.
25 14 Numerical case study Consumer s decision problem [ ˆb J i = E(G(q i ; b i ) π ri i,3 ( ˆb i, ˆqi, qi ] = arg max i, ˆq i, q i )) actual consumption declared reduction capacity reported baseline What are the consumer s optimal decisions under this DR contract?
26 14 Numerical case study Consumer s decision problem [ ˆb J i = E(G(q i ; b i ) π ri i,3 ( ˆb i, ˆqi, qi ] = arg max i, ˆq i, q i )) actual consumption declared reduction capacity reported baseline What are the consumer s optimal decisions under this DR contract? Simulation info: The retail price is p = 0.26 $/kwh. True baseline is b i = 8 kwh. The incentive/penalty price is p 2 = 0.3 $/kwh. The marginal utility is γ i = 0.05 $/kwh 2. The maximum allowable consumption is q max,i = 16 kwh. A Monte Carlo simulation is performed with 1000 realizations of r i for each value of probability.
27 Results: optimal consumer s choice (p ri of call) is the probability Consumer's optimal choices (kwh) ˆb i q i,ri=0 ˆq i q i,ri= p ri Aggregator should call for DR to users with a probability of call between zero and the threshold one to limit gaming opportunities (asymptotic truthfulness for baseline). An agent declares what he is willing to reduce according to his true preferences ˆq i = q i,r i =1 irrespective of the probability of call. 15
28 Results: Percentage of gaming limitation on the reported baseline (p ri is the probability of call) ˆbi/bi p ri For instance, an aggregator calls a group of agents with a probability of call equals to p ri = 0.1 then rational consumers have incentives to overreport the baseline by an 11%. 16
29 17 Results: Optimal expected profit of a consumer (p ri probability of call). is the DR No DR Optimal expected profit ($) p ri The user s benefit when he participates in this contract is at least as good as when he does not join in the incentive-based DR program (voluntary participation).
30 Results: Threshold probability of call (p ri of call) is the probability Threshold probability of call p 2 /p If p = p 2, the critical point is 0.5 of likelihood. The threshold probability is designed by the aggregator through the selection of prices. 18
31 19 Conclusions DR contract was proposed which induces voluntary participation and truthfulness based on the probability of call. The main goal of the aggregator is to call a subset of users that meets the probability criterion. A contract for incentive-based DR based on probability of call enables to limit the gaming opportunities. Advantages: no computation by aggregator, information exchange in terms of energy, easy to understand by agents, implementable.
32 20 References I Chao, H., Demand response in wholesale electricity markets: the choice of customer baseline. Journal of Regulatory Economics 39 (1), Severin Borenstein, Peak-Time Rebates: Money for Nothing? URL Peak-Time-Rebates-Money-for-Nothing Vuelvas, J., Ruiz, F., Rational consumer decisions in a peak time rebate program. Electric Power Systems Research 143,
33 Go back to PowerPoint!!! 21
EC476 Contracts and Organizations, Part III: Lecture 2
EC476 Contracts and Organizations, Part III: Lecture 2 Leonardo Felli 32L.G.06 19 January 2015 Moral Hazard: Consider the contractual relationship between two agents (a principal and an agent) The principal
More informationOptimal Demand Response
Optimal Demand Response Libin Jiang Steven Low Computing + Math Sciences Electrical Engineering Caltech June 2011 Outline o Motivation o Demand response model o Some results Wind power over land (outside
More informationGame Theory, Information, Incentives
Game Theory, Information, Incentives Ronald Wendner Department of Economics Graz University, Austria Course # 320.501: Analytical Methods (part 6) The Moral Hazard Problem Moral hazard as a problem of
More informationVickrey Auction. Mechanism Design. Algorithmic Game Theory. Alexander Skopalik Algorithmic Game Theory 2013 Mechanism Design
Algorithmic Game Theory Vickrey Auction Vickrey-Clarke-Groves Mechanisms Mechanisms with Money Player preferences are quantifiable. Common currency enables utility transfer between players. Preference
More informationOptimal Demand Response
Optimal Demand Response Libin Jiang Steven Low Computing + Math Sciences Electrical Engineering Caltech Oct 2011 Outline Caltech smart grid research Optimal demand response Global trends 1 Exploding renewables
More informationMicroeconomics II Lecture 4: Incomplete Information Karl Wärneryd Stockholm School of Economics November 2016
Microeconomics II Lecture 4: Incomplete Information Karl Wärneryd Stockholm School of Economics November 2016 1 Modelling incomplete information So far, we have studied games in which information was complete,
More informationFirming Renewable Power with Demand Response: An End-to-end Aggregator Business Model
Firming Renewable Power with Demand Response: An End-to-end Aggregator Business Model Clay Campaigne joint work with Shmuel Oren November 5, 2015 1 / 16 Motivation Problem: Expansion of renewables increases
More information5. Externalities and Public Goods. Externalities. Public Goods types. Public Goods
5. Externalities and Public Goods 5. Externalities and Public Goods Externalities Welfare properties of Walrasian Equilibria rely on the hidden assumption of private goods: the consumption of the good
More informationBayesian Games and Mechanism Design Definition of Bayes Equilibrium
Bayesian Games and Mechanism Design Definition of Bayes Equilibrium Harsanyi [1967] What happens when players do not know one another s payoffs? Games of incomplete information versus games of imperfect
More information5. Externalities and Public Goods
5. Externalities and Public Goods Welfare properties of Walrasian Equilibria rely on the hidden assumption of private goods: the consumption of the good by one person has no effect on other people s utility,
More informationNTU IO (I) : Auction Theory and Mechanism Design II Groves Mechanism and AGV Mechansim. u i (x, t i, θ i ) = V i (x, θ i ) + t i,
Meng-Yu Liang NTU O : Auction Theory and Mechanism Design Groves Mechanism and AGV Mechansim + 1 players. Types are drawn from independent distribution P i on [θ i, θ i ] with strictly positive and differentiable
More informationIntroduction: Asymmetric Information and the Coase Theorem
BGPE Intensive Course: Contracts and Asymmetric Information Introduction: Asymmetric Information and the Coase Theorem Anke Kessler Anke Kessler p. 1/?? Introduction standard neoclassical economic theory
More informationOn revealed preferences in oligopoly games
University of Manchester, UK November 25, 2010 Introduction Suppose we make a finite set of observations T = {1,..., m}, m 1, of a perfectly homogeneous-good oligopoly market. There is a finite number
More informationRedistribution Mechanisms for Assignment of Heterogeneous Objects
Redistribution Mechanisms for Assignment of Heterogeneous Objects Sujit Gujar Dept of Computer Science and Automation Indian Institute of Science Bangalore, India sujit@csa.iisc.ernet.in Y Narahari Dept
More informationDepartment of Agricultural Economics. PhD Qualifier Examination. May 2009
Department of Agricultural Economics PhD Qualifier Examination May 009 Instructions: The exam consists of six questions. You must answer all questions. If you need an assumption to complete a question,
More informationVickrey Auction VCG Characterization. Mechanism Design. Algorithmic Game Theory. Alexander Skopalik Algorithmic Game Theory 2013 Mechanism Design
Algorithmic Game Theory Vickrey Auction Vickrey-Clarke-Groves Mechanisms Characterization of IC Mechanisms Mechanisms with Money Player preferences are quantifiable. Common currency enables utility transfer
More informationEconS Microeconomic Theory II Homework #9 - Answer key
EconS 503 - Microeconomic Theory II Homework #9 - Answer key 1. WEAs with market power. Consider an exchange economy with two consumers, A and B, whose utility functions are u A (x A 1 ; x A 2 ) = x A
More informationBasics of Game Theory
Basics of Game Theory Giacomo Bacci and Luca Sanguinetti Department of Information Engineering University of Pisa, Pisa, Italy {giacomo.bacci,luca.sanguinetti}@iet.unipi.it April - May, 2010 G. Bacci and
More informationMicroeconomic Theory (501b) Problem Set 10. Auctions and Moral Hazard Suggested Solution: Tibor Heumann
Dirk Bergemann Department of Economics Yale University Microeconomic Theory (50b) Problem Set 0. Auctions and Moral Hazard Suggested Solution: Tibor Heumann 4/5/4 This problem set is due on Tuesday, 4//4..
More informationx ax 1 2 bx2 a bx =0 x = a b. Hence, a consumer s willingness-to-pay as a function of liters on sale, 1 2 a 2 2b, if l> a. (1)
Answers to Exam Economics 201b First Half 1. (a) Observe, first, that no consumer ever wishes to consume more than 3/2 liters (i.e., 1.5 liters). To see this, observe that, even if the beverage were free,
More informationInformed Principal in Private-Value Environments
Informed Principal in Private-Value Environments Tymofiy Mylovanov Thomas Tröger University of Bonn June 21, 2008 1/28 Motivation 2/28 Motivation In most applications of mechanism design, the proposer
More informationGame Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India July 2012
Game Theory Lecture Notes By Y. Narahari Department of Computer Science and Automation Indian Institute of Science Bangalore, India July 2012 The Vickrey-Clarke-Groves Mechanisms Note: This is a only a
More informationDepartment of Economics The Ohio State University Final Exam Questions and Answers Econ 8712
Prof. Peck Fall 20 Department of Economics The Ohio State University Final Exam Questions and Answers Econ 872. (0 points) The following economy has two consumers, two firms, and three goods. Good is leisure/labor.
More informationChapter 5. Transmission networks and electricity markets
Chapter 5. Transmission networks and electricity markets 1 Introduction In most of the regions of the world: assumptions that electrical energy can be traded as if all generators were connected to the
More informationA Merchant Mechanism for Electricity Transmission Expansion
A Merchant Mechanism for Electricity Transmission Expansion Tarjei Kristiansen, Norwegian University of Science and Technology. Tarjei.Kristiansen@elkraft.ntnu.no Juan Rosellon, Harvard University/CIDE.
More informationJavier Contreras Sanz- Universidad de Castilla-La Mancha Jesús María López Lezama- Universidad de Antioquia Antonio Padilha-Feltrin- Universidade
Javier Contreras Sanz- Universidad de Castilla-La Mancha Jesús María López Lezama- Universidad de Antioquia Antonio Padilha-Feltrin- Universidade Estadual Paulista Jose Ignacio Muñoz-Universidad de Castilla-La
More informationIncreasingly, economists are asked not just to study or explain or interpret markets, but to design them.
What is market design? Increasingly, economists are asked not just to study or explain or interpret markets, but to design them. This requires different tools and ideas than neoclassical economics, which
More informationRevenue Maximization in a Cloud Federation
Revenue Maximization in a Cloud Federation Makhlouf Hadji and Djamal Zeghlache September 14th, 2015 IRT SystemX/ Telecom SudParis Makhlouf Hadji Outline of the presentation 01 Introduction 02 03 04 05
More informationAdvanced Microeconomics
Advanced Microeconomics Leonardo Felli EC441: Room D.106, Z.332, D.109 Lecture 8 bis: 24 November 2004 Monopoly Consider now the pricing behavior of a profit maximizing monopolist: a firm that is the only
More informationNumerical illustration
A umerical illustration Inverse demand is P q, t = a 0 a 1 e λ 2t bq, states of the world are distributed according to f t = λ 1 e λ 1t, and rationing is anticipated and proportional. a 0, a 1, λ = λ 1
More informationMechanism Design II. Terence Johnson. University of Notre Dame. Terence Johnson (ND) Mechanism Design II 1 / 30
Mechanism Design II Terence Johnson University of Notre Dame Terence Johnson (ND) Mechanism Design II 1 / 30 Mechanism Design Recall: game theory takes the players/actions/payoffs as given, and makes predictions
More informationDeceptive Advertising with Rational Buyers
Deceptive Advertising with Rational Buyers September 6, 016 ONLINE APPENDIX In this Appendix we present in full additional results and extensions which are only mentioned in the paper. In the exposition
More informationFINM6900 Finance Theory Noisy Rational Expectations Equilibrium for Multiple Risky Assets
FINM69 Finance Theory Noisy Rational Expectations Equilibrium for Multiple Risky Assets February 3, 212 Reference Anat R. Admati, A Noisy Rational Expectations Equilibrium for Multi-Asset Securities Markets,
More informationPrice Discrimination through Refund Contracts in Airlines
Introduction Price Discrimination through Refund Contracts in Airlines Paan Jindapon Department of Economics and Finance The University of Texas - Pan American Department of Economics, Finance and Legal
More informationMechanism design and allocation algorithms for energy-network markets with piece-wise linear costs and quadratic externalities
1 / 45 Mechanism design and allocation algorithms for energy-network markets with piece-wise linear costs and quadratic externalities Alejandro Jofré 1 Center for Mathematical Modeling & DIM Universidad
More informationMotivation. Game Theory 24. Mechanism Design. Setting. Preference relations contain no information about by how much one candidate is preferred.
Motivation Game Theory 24. Mechanism Design Preference relations contain no information about by how much one candidate is preferred. Idea: Use money to measure this. Albert-Ludwigs-Universität Freiburg
More informationTheory of Auctions. Carlos Hurtado. Jun 23th, Department of Economics University of Illinois at Urbana-Champaign
Theory of Auctions Carlos Hurtado Department of Economics University of Illinois at Urbana-Champaign hrtdmrt2@illinois.edu Jun 23th, 2015 C. Hurtado (UIUC - Economics) Game Theory On the Agenda 1 Formalizing
More informationVickrey-Clarke-Groves Mechanisms
Vickrey-Clarke-Groves Mechanisms Jonathan Levin 1 Economics 285 Market Design Winter 2009 1 These slides are based on Paul Milgrom s. onathan Levin VCG Mechanisms Winter 2009 1 / 23 Motivation We consider
More informationAlgorithmic Game Theory Introduction to Mechanism Design
Algorithmic Game Theory Introduction to Mechanism Design Makis Arsenis National Technical University of Athens April 216 Makis Arsenis (NTUA) AGT April 216 1 / 41 Outline 1 Social Choice Social Choice
More informationGame Theory. Monika Köppl-Turyna. Winter 2017/2018. Institute for Analytical Economics Vienna University of Economics and Business
Monika Köppl-Turyna Institute for Analytical Economics Vienna University of Economics and Business Winter 2017/2018 Static Games of Incomplete Information Introduction So far we assumed that payoff functions
More informationEconS Advanced Microeconomics II Handout on Mechanism Design
EconS 503 - Advanced Microeconomics II Handout on Mechanism Design 1. Public Good Provision Imagine that you and your colleagues want to buy a co ee machine for your o ce. Suppose that some of you may
More informationRegulation Under Asymmetric Information
Regulation Under Asymmetric Information Lecture 5: Course 608 Sugata Bag Delhi School of Economics February 2013 Sugata Bag (DSE) Regulation Under Asymmetric Information 08/02 1 / 50 Basic Concepts The
More informationVirtual Robust Implementation and Strategic Revealed Preference
and Strategic Revealed Preference Workshop of Mathematical Economics Celebrating the 60th birthday of Aloisio Araujo IMPA Rio de Janeiro December 2006 Denitions "implementation": requires ALL equilibria
More informationSolution: Since the prices are decreasing, we consider all the nested options {1,..., i}. Given such a set, the expected revenue is.
Problem 1: Choice models and assortment optimization Consider a MNL choice model over five products with prices (p1,..., p5) = (7, 6, 4, 3, 2) and preference weights (i.e., MNL parameters) (v1,..., v5)
More informationInformation Choice in Macroeconomics and Finance.
Information Choice in Macroeconomics and Finance. Laura Veldkamp New York University, Stern School of Business, CEPR and NBER Spring 2009 1 Veldkamp What information consumes is rather obvious: It consumes
More informationA technical appendix for multihoming and compatibility
A technical appendix for multihoming and compatibility Toker Doganoglu and Julian Wright July 19, 2005 We would like to thank two anonymous referees and our editor, Simon Anderson, for their very helpful
More informationContracts under Asymmetric Information
Contracts under Asymmetric Information 1 I Aristotle, economy (oiko and nemo) and the idea of exchange values, subsequently adapted by Ricardo and Marx. Classical economists. An economy consists of a set
More informationEfficiency and Braess Paradox under Pricing
Efficiency and Braess Paradox under Pricing Asuman Ozdaglar Joint work with Xin Huang, [EECS, MIT], Daron Acemoglu [Economics, MIT] October, 2004 Electrical Engineering and Computer Science Dept. Massachusetts
More informationCrowdsourcing contests
December 8, 2012 Table of contents 1 Introduction 2 Related Work 3 Model: Basics 4 Model: Participants 5 Homogeneous Effort 6 Extensions Table of Contents 1 Introduction 2 Related Work 3 Model: Basics
More information4. CONTINUOUS VARIABLES AND ECONOMIC APPLICATIONS
STATIC GAMES 4. CONTINUOUS VARIABLES AND ECONOMIC APPLICATIONS Universidad Carlos III de Madrid CONTINUOUS VARIABLES In many games, ure strategies that layers can choose are not only, 3 or any other finite
More informationAn Optimal Bidimensional Multi Armed Bandit Auction for Multi unit Procurement
An Optimal Bidimensional Multi Armed Bandit Auction for Multi unit Procurement Satyanath Bhat Joint work with: Shweta Jain, Sujit Gujar, Y. Narahari Department of Computer Science and Automation, Indian
More informationGS/ECON 5010 Answers to Assignment 3 W2005
GS/ECON 500 Answers to Assignment 3 W005 Q. What are the market price, and aggregate quantity sold, in long run equilibrium in a perfectly competitive market f which the demand function has the equation
More informationCongestion Equilibrium for Differentiated Service Classes Richard T. B. Ma
Congestion Equilibrium for Differentiated Service Classes Richard T. B. Ma School of Computing National University of Singapore Allerton Conference 2011 Outline Characterize Congestion Equilibrium Modeling
More informationMulti-object auctions (and matching with money)
(and matching with money) Introduction Many auctions have to assign multiple heterogenous objects among a group of heterogenous buyers Examples: Electricity auctions (HS C 18:00), auctions of government
More informationWireless Network Pricing Chapter 6: Oligopoly Pricing
Wireless Network Pricing Chapter 6: Oligopoly Pricing Jianwei Huang & Lin Gao Network Communications and Economics Lab (NCEL) Information Engineering Department The Chinese University of Hong Kong Huang
More informationTrade policy III: Export subsidies
The Vienna Institute for International Economic Studies - wiiw June 25, 2015 Overview Overview 1 1 Under perfect competition lead to welfare loss 2 Effects depending on market structures 1 Subsidies to
More informationEx-ante and Ex-post Subcontracting in Highway Procurement Markets
Ex-ante and Ex-post Subcontracting in Highway Procurement Markets Jorge Balat Tatiana Komarova Elena Krasnokutskaya December 11, 2017 Preliminary and Incomplete. Do not circulate. Abstract This paper provides
More informationAddendum to: Dual Sales Channel Management with Service Competition
Addendum to: Dual Sales Channel Management with Service Competition Kay-Yut Chen Murat Kaya Özalp Özer Management Science & Engineering, Stanford University, Stanford, CA. December, 006 1. Single-Channel
More informationGame Theory: Spring 2017
Game Theory: Spring 2017 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today In this second lecture on mechanism design we are going to generalise
More informationLecture 10: Mechanism Design
Computational Game Theory Spring Semester, 2009/10 Lecture 10: Mechanism Design Lecturer: Yishay Mansour Scribe: Vera Vsevolozhsky, Nadav Wexler 10.1 Mechanisms with money 10.1.1 Introduction As we have
More informationKnapsack Auctions. Sept. 18, 2018
Knapsack Auctions Sept. 18, 2018 aaacehicbvdlssnafj34rpuvdelmsigvskmkoasxbtcuk9ghncfmjpn26gqszibsevojbvwvny4ucevsnx/jpm1cww8mndnnxu69x08ylcqyvo2v1bx1jc3svnl7z3dv3zw47mg4fzi0ccxi0forjixy0lzumdjlbegrz0jxh93kfvebceljfq8mcxejnoa0pbgplxnmwxxs2tu49ho16lagvjl/8hpoua6dckkhrizrtt2zytwtgeaysqtsaqvanvnlbdfoi8ivzkjkvm0lys2qubqzmi07qsqjwim0ih1noyqidlpzqvn4qpuahrhqjys4u393zcischl5ujjfus56ufif109veovmlcepihzpb4upgyqgetowoijgxsaaicyo3hxiiriik51hwydgl568tdqnum3v7bulsvo6ikmejsejqaibxiimuaut0ayypijn8arejcfjxxg3pualk0brcwt+wpj8acrvmze=
More informationKnowing What Others Know: Coordination Motives in Information Acquisition
Knowing What Others Know: Coordination Motives in Information Acquisition Christian Hellwig and Laura Veldkamp UCLA and NYU Stern May 2006 1 Hellwig and Veldkamp Two types of information acquisition Passive
More informationOPTIMIZATION. joint course with. Ottimizzazione Discreta and Complementi di R.O. Edoardo Amaldi. DEIB Politecnico di Milano
OPTIMIZATION joint course with Ottimizzazione Discreta and Complementi di R.O. Edoardo Amaldi DEIB Politecnico di Milano edoardo.amaldi@polimi.it Website: http://home.deib.polimi.it/amaldi/opt-15-16.shtml
More informationThe Lucas Imperfect Information Model
The Lucas Imperfect Information Model Based on the work of Lucas (972) and Phelps (970), the imperfect information model represents an important milestone in modern economics. The essential idea of the
More informationMechanism Design. Christoph Schottmüller / 27
Mechanism Design Christoph Schottmüller 2015-02-25 1 / 27 Outline 1 Bayesian implementation and revelation principle 2 Expected externality mechanism 3 Review questions and exercises 2 / 27 Bayesian implementation
More informationIndustrial Organization II (ECO 2901) Winter Victor Aguirregabiria. Problem Set #1 Due of Friday, March 22, 2013
Industrial Organization II (ECO 2901) Winter 2013. Victor Aguirregabiria Problem Set #1 Due of Friday, March 22, 2013 TOTAL NUMBER OF POINTS: 200 PROBLEM 1 [30 points]. Considertheestimationofamodelofdemandofdifferentiated
More informationOn the Pareto Efficiency of a Socially Optimal Mechanism for Monopoly Regulation
MPRA Munich Personal RePEc Archive On the Pareto Efficiency of a Socially Optimal Mechanism for Monopoly Regulation Ismail Saglam Ipek University 4 May 2016 Online at https://mpra.ub.uni-muenchen.de/71090/
More informationTopics of Algorithmic Game Theory
COMP323 Introduction to Computational Game Theory Topics of Algorithmic Game Theory Paul G. Spirakis Department of Computer Science University of Liverpool Paul G. Spirakis (U. Liverpool) Topics of Algorithmic
More informationFull Surplus Extraction and Costless Information Revelation in Dynamic Environments. Shunya NODA (University of Tokyo)
Full Surplus Extraction and Costless Information Revelation in Dynamic Environments Shunya NODA (University of Tokyo) Outline 1. Introduction. Two-Period Example 3. Three-Period Example 4. Model 5. Main
More informationDecision Models Lecture 5 1. Lecture 5. Foreign-Currency Trading Integer Programming Plant-location example Summary and Preparation for next class
Decision Models Lecture 5 1 Lecture 5 Foreign-Currency Trading Integer Programming Plant-location example Summary and Preparation for next class Foreign Exchange (FX) Markets Decision Models Lecture 5
More informationReal-Time Demand Response with Uncertain Renewable Energy in Smart Grid
Forty-Ninth Annual Allerton Conference Allerton House, UIUC, Illinois, USA September 28-3, 211 Real-Time Demand Response with Uncertain Renewable Energy in Smart Grid Libin Jiang and Steven Low Engineering
More informationGame theory lecture 4. September 24, 2012
September 24, 2012 Finding Nash equilibrium Best-response or best-reply functions. We introduced Nash-equilibrium as a profile of actions (an action for each player) such that no player has an incentive
More informationAlgorithmic Game Theory and Applications
Algorithmic Game Theory and Applications Lecture 18: Auctions and Mechanism Design II: a little social choice theory, the VCG Mechanism, and Market Equilibria Kousha Etessami Reminder: Food for Thought:
More informationFirms and returns to scale -1- Firms and returns to scale
Firms and returns to scale -1- Firms and returns to scale. Increasing returns to scale and monopoly pricing 2. Constant returns to scale 19 C. The CRS economy 25 D. pplication to trade 47 E. Decreasing
More informationMarket-Based Environmental Policy and Ecosystem Services Delivery
1 Market-Based Environmental Policy and Ecosystem Services Delivery ECO-DELIVERY European Investment Bank University Research Sponsorship (EIBURS) Financial and Economic Valuation of Environmental Impacts
More informationEcon 101A Problem Set 6 Solutions Due on Monday Dec. 9. No late Problem Sets accepted, sorry!
Econ 0A Problem Set 6 Solutions Due on Monday Dec. 9. No late Problem Sets accepted, sry! This Problem set tests the knowledge that you accumulated mainly in lectures 2 to 26. The problem set is focused
More informationSelling Experiments. Dirk Bergemann 1 Alessandro Bonatti 2 Alex Smolin 1. BFI Chicago July 10th, Yale University.
Selling Experiments Dirk Bergemann 1 Alessandro Bonatti 2 Alex Smolin 1 1 Yale University 2 MIT Sloan BFI Chicago July 10th, 2015 Introduction A decision maker under uncertainty: has partial (private)
More informationInefficient Equilibria of Second-Price/English Auctions with Resale
Inefficient Equilibria of Second-Price/English Auctions with Resale Rod Garratt, Thomas Tröger, and Charles Zheng September 29, 2006 Abstract In second-price or English auctions involving symmetric, independent,
More informationArea I: Contract Theory Question (Econ 206)
Theory Field Exam Summer 2011 Instructions You must complete two of the four areas (the areas being (I) contract theory, (II) game theory A, (III) game theory B, and (IV) psychology & economics). Be sure
More informationDynamic Discrete Choice Structural Models in Empirical IO
Dynamic Discrete Choice Structural Models in Empirical IO Lecture 4: Euler Equations and Finite Dependence in Dynamic Discrete Choice Models Victor Aguirregabiria (University of Toronto) Carlos III, Madrid
More informationFirms and returns to scale -1- John Riley
Firms and returns to scale -1- John Riley Firms and returns to scale. Increasing returns to scale and monopoly pricing 2. Natural monopoly 1 C. Constant returns to scale 21 D. The CRS economy 26 E. pplication
More informationMoral Hazard: Part 1. April 9, 2018
Moral Hazard: Part 1 April 9, 2018 Introduction In a standard moral hazard problem, the agent A is characterized by only one type. As with adverse selection, the principal P wants to engage in an economic
More informationECON4515 Finance theory 1 Diderik Lund, 5 May Perold: The CAPM
Perold: The CAPM Perold starts with a historical background, the development of portfolio theory and the CAPM. Points out that until 1950 there was no theory to describe the equilibrium determination of
More information18.600: Lecture 3 What is probability?
18.600: Lecture 3 What is probability? Scott Sheffield MIT Outline Formalizing probability Sample space DeMorgan s laws Axioms of probability Outline Formalizing probability Sample space DeMorgan s laws
More informationTrain the model with a subset of the data. Test the model on the remaining data (the validation set) What data to choose for training vs. test?
Train the model with a subset of the data Test the model on the remaining data (the validation set) What data to choose for training vs. test? In a time-series dimension, it is natural to hold out the
More informationDistributed Algorithmic Mechanism Design: Recent Results and Future Directions
Distributed Algorithmic Mechanism Design: Recent Results and Future Directions Joan Feigenbaum Yale Scott Shenker ICSI Slides: http://www.cs.yale.edu/~jf/dialm02.{ppt,pdf} Paper: http://www.cs.yale.edu/~jf/fs.{ps,pdf}
More informationBattery Energy Storage
Battery Energy Storage Implications for Load Shapes and Forecasting April 28, 2017 TOPICS» What is Energy Storage» Storage Market, Costs, Regulatory Background» Behind the Meter (BTM) Battery Storage Where
More informationDesign Patent Damages under Sequential Innovation
Design Patent Damages under Sequential Innovation Yongmin Chen and David Sappington University of Colorado and University of Florida February 2016 1 / 32 1. Introduction Patent policy: patent protection
More informationDuration and deadline differentiated demand: a model of flexible demand
Duration and deadline differentiated demand: a model of flexible demand A. Nayyar, M. Negrete-Pincetić, K. Poolla, W. Chen, Y.Mo, L. Qiu, P. Varaiya May, 2016 1 / 21 Outline Duration-differentiated (DD)
More informationCompetitive Equilibrium
Competitive Equilibrium Econ 2100 Fall 2017 Lecture 16, October 26 Outline 1 Pareto Effi ciency 2 The Core 3 Planner s Problem(s) 4 Competitive (Walrasian) Equilibrium Decentralized vs. Centralized Economic
More informationA Bayesian Perspective on Residential Demand Response Using Smart Meter Data
A Bayesian Perspective on Residential Demand Response Using Smart Meter Data Datong-Paul Zhou, Maximilian Balandat, and Claire Tomlin University of California, Berkeley [datong.zhou, balandat, tomlin]@eecs.berkeley.edu
More informationImplementability, Walrasian Equilibria, and Efficient Matchings
Implementability, Walrasian Equilibria, and Efficient Matchings Piotr Dworczak and Anthony Lee Zhang Abstract In general screening problems, implementable allocation rules correspond exactly to Walrasian
More informationSource: US. Bureau of Economic Analysis Shaded areas indicate US recessions research.stlouisfed.org
Business Cycles 0 Real Gross Domestic Product 18,000 16,000 (Billions of Chained 2009 Dollars) 14,000 12,000 10,000 8,000 6,000 4,000 2,000 1940 1960 1980 2000 Source: US. Bureau of Economic Analysis Shaded
More informationSimplifying this, we obtain the following set of PE allocations: (x E ; x W ) 2
Answers Answer for Q (a) ( pts:.5 pts. for the de nition and.5 pts. for its characterization) The de nition of PE is standard. There may be many ways to characterize the set of PE allocations. But whichever
More informationEnforcing Truthful-Rating Equilibria in Electronic Marketplaces
Enforcing Truthful-Rating Equilibria in Electronic Marketplaces T. G. Papaioannou and G. D. Stamoulis Networks Economics & Services Lab, Dept.of Informatics, Athens Univ. of Economics & Business (AUEB
More informationMechanism Design: Basic Concepts
Advanced Microeconomic Theory: Economics 521b Spring 2011 Juuso Välimäki Mechanism Design: Basic Concepts The setup is similar to that of a Bayesian game. The ingredients are: 1. Set of players, i {1,
More information4. Partial Equilibrium under Imperfect Competition
4. Partial Equilibrium under Imperfect Competition Partial equilibrium studies the existence of equilibrium in the market of a given commodity and analyzes its properties. Prices in other markets as well
More informationPhD Qualifier Examination
PhD Qualifier Examination Department of Agricultural Economics July 26, 2013 Instructions The exam consists of six questions. You must answer all questions. If you need an assumption to complete a question,
More informationBounded Rationality Lecture 2. Full (Substantive, Economic) Rationality
Bounded Rationality Lecture 2 Full (Substantive, Economic) Rationality Mikhail Anufriev EDG, Faculty of Business, University of Technology Sydney (UTS) European University at St.Petersburg Faculty of Economics
More informationNoncooperative Games, Couplings Constraints, and Partial Effi ciency
Noncooperative Games, Couplings Constraints, and Partial Effi ciency Sjur Didrik Flåm University of Bergen, Norway Background Customary Nash equilibrium has no coupling constraints. Here: coupling constraints
More information