48,221 research outputs found
Data-Driven Estimation in Equilibrium Using Inverse Optimization
Equilibrium modeling is common in a variety of fields such as game theory and
transportation science. The inputs for these models, however, are often
difficult to estimate, while their outputs, i.e., the equilibria they are meant
to describe, are often directly observable. By combining ideas from inverse
optimization with the theory of variational inequalities, we develop an
efficient, data-driven technique for estimating the parameters of these models
from observed equilibria. We use this technique to estimate the utility
functions of players in a game from their observed actions and to estimate the
congestion function on a road network from traffic count data. A distinguishing
feature of our approach is that it supports both parametric and
\emph{nonparametric} estimation by leveraging ideas from statistical learning
(kernel methods and regularization operators). In computational experiments
involving Nash and Wardrop equilibria in a nonparametric setting, we find that
a) we effectively estimate the unknown demand or congestion function,
respectively, and b) our proposed regularization technique substantially
improves the out-of-sample performance of our estimators.Comment: 36 pages, 5 figures Additional theorems for generalization guarantees
and statistical analysis adde
Learning from past bids to participate strategically in day-ahead electricity markets
We consider the process of bidding by electricity suppliers in a day-ahead market context, where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent pricesFirst author draf
Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets
We consider the process of bidding by electricity suppliers in a day-ahead
market context where each supplier bids a linear non-decreasing function of her
generating capacity with the goal of maximizing her individual profit given
other competing suppliers' bids. Based on the submitted bids, the market
operator schedules suppliers to meet demand during each hour and determines
hourly market clearing prices. Eventually, this game-theoretic process reaches
a Nash equilibrium when no supplier is motivated to modify her bid. However,
solving the individual profit maximization problem requires information of
rivals' bids, which are typically not available. To address this issue, we
develop an inverse optimization approach for estimating rivals' production cost
functions given historical market clearing prices and production levels. We
then use these functions to bid strategically and compute Nash equilibrium
bids. We present numerical experiments illustrating our methodology, showing
good agreement between bids based on the estimated production cost functions
with the bids based on the true cost functions. We discuss an extension of our
approach that takes into account network congestion resulting in
location-dependent prices
The price of anarchy in transportation networks by estimating user cost functions from actual traffic data
We have considered a large-scale road network in Eastern Massachusetts. Using real traffic data in the form of spatial average speeds and the flow capacity for each road segment of the network, we converted the speed data to flow data and estimated the origin-destination flow demand matrices for the network. Assuming that the observed traffic data correspond to user (Wardrop) equilibria for different times-of-the-day and days-of-the-week, we formulated appropriate inverse problems to recover the per-road cost (congestion) functions determining user route selection for each month and time-of-day period. In addition, we analyzed the sensitivity of the total user latency cost to important parameters such as road capacities and minimum travel times. Finally, we formulated a system-optimum problem in order to find socially optimal flows for the network. We investigated the network performance, in terms of the total latency, under a user-optimal policy versus a system-optimal policy. The ratio of these two quantities is defined as the Price of Anarchy (POA) and quantifies the efficiency loss of selfish actions compared to socially optimal ones. Our findings contribute to efforts for a smarter and more efficient city
Data-driven Estimation of Origin-Destination Demand and User Cost Functions for the Optimization of Transportation Networks
In earlier work (Zhang et al., 2016) we used actual traffic data from the
Eastern Massachusetts transportation network in the form of spatial average
speeds and road segment flow capacities in order to estimate Origin-Destination
(OD) flow demand matrices for the network. Based on a Traffic Assignment
Problem (TAP) formulation (termed "forward problem"), in this paper we use a
scheme similar to our earlier work to estimate initial OD demand matrices and
then propose a new inverse problem formulation in order to estimate user cost
functions. This new formulation allows us to efficiently overcome numerical
difficulties that limited our prior work to relatively small subnetworks and,
assuming the travel latency cost functions are available, to adjust the values
of the OD demands accordingly so that the flow observations are as close as
possible to the solutions of the forward problem. We also derive sensitivity
analysis results for the total user latency cost with respect to important
parameters such as road capacities and minimum travel times. Finally, using the
same actual traffic data from the Eastern Massachusetts transportation network,
we quantify the Price of Anarchy (POA) for a much larger network than that in
Zhang et al. (2016).Comment: Preprint submitted to The 20th World Congress of the International
Federation of Automatic Control, July 9-14, 2017, Toulouse, Franc
On the Inversion of High Energy Proton
Inversion of the K-fold stochastic autoconvolution integral equation is an
elementary nonlinear problem, yet there are no de facto methods to solve it
with finite statistics. To fix this problem, we introduce a novel inverse
algorithm based on a combination of minimization of relative entropy, the Fast
Fourier Transform and a recursive version of Efron's bootstrap. This gives us
power to obtain new perspectives on non-perturbative high energy QCD, such as
probing the ab initio principles underlying the approximately negative binomial
distributions of observed charged particle final state multiplicities, related
to multiparton interactions, the fluctuating structure and profile of proton
and diffraction. As a proof-of-concept, we apply the algorithm to ALICE
proton-proton charged particle multiplicity measurements done at different
center-of-mass energies and fiducial pseudorapidity intervals at the LHC,
available on HEPData. A strong double peak structure emerges from the
inversion, barely visible without it.Comment: 29 pages, 10 figures, v2: extended analysis (re-projection ratios,
2D
A tutorial on recursive models for analyzing and predicting path choice behavior
The problem at the heart of this tutorial consists in modeling the path
choice behavior of network users. This problem has been extensively studied in
transportation science, where it is known as the route choice problem. In this
literature, individuals' choice of paths are typically predicted using discrete
choice models. This article is a tutorial on a specific category of discrete
choice models called recursive, and it makes three main contributions: First,
for the purpose of assisting future research on route choice, we provide a
comprehensive background on the problem, linking it to different fields
including inverse optimization and inverse reinforcement learning. Second, we
formally introduce the problem and the recursive modeling idea along with an
overview of existing models, their properties and applications. Third, we
extensively analyze illustrative examples from different angles so that a
novice reader can gain intuition on the problem and the advantages provided by
recursive models in comparison to path-based ones
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