19,493 research outputs found
Betting and Belief: Prediction Markets and Attribution of Climate Change
Despite much scientific evidence, a large fraction of the American public
doubts that greenhouse gases are causing global warming. We present a
simulation model as a computational test-bed for climate prediction markets.
Traders adapt their beliefs about future temperatures based on the profits of
other traders in their social network. We simulate two alternative climate
futures, in which global temperatures are primarily driven either by carbon
dioxide or by solar irradiance. These represent, respectively, the scientific
consensus and a hypothesis advanced by prominent skeptics. We conduct
sensitivity analyses to determine how a variety of factors describing both the
market and the physical climate may affect traders' beliefs about the cause of
global climate change. Market participation causes most traders to converge
quickly toward believing the "true" climate model, suggesting that a climate
market could be useful for building public consensus.Comment: All code and data for the model is available at
http://johnjnay.com/predMarket/. Forthcoming in Proceedings of the 2016
Winter Simulation Conference. IEEE Pres
TRIDEnT: Building Decentralized Incentives for Collaborative Security
Sophisticated mass attacks, especially when exploiting zero-day
vulnerabilities, have the potential to cause destructive damage to
organizations and critical infrastructure. To timely detect and contain such
attacks, collaboration among the defenders is critical. By correlating
real-time detection information (alerts) from multiple sources (collaborative
intrusion detection), defenders can detect attacks and take the appropriate
defensive measures in time. However, although the technical tools to facilitate
collaboration exist, real-world adoption of such collaborative security
mechanisms is still underwhelming. This is largely due to a lack of trust and
participation incentives for companies and organizations. This paper proposes
TRIDEnT, a novel collaborative platform that aims to enable and incentivize
parties to exchange network alert data, thus increasing their overall detection
capabilities. TRIDEnT allows parties that may be in a competitive relationship,
to selectively advertise, sell and acquire security alerts in the form of
(near) real-time peer-to-peer streams. To validate the basic principles behind
TRIDEnT, we present an intuitive game-theoretic model of alert sharing, that is
of independent interest, and show that collaboration is bound to take place
infinitely often. Furthermore, to demonstrate the feasibility of our approach,
we instantiate our design in a decentralized manner using Ethereum smart
contracts and provide a fully functional prototype.Comment: 28 page
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy
management mechanism for the smart grid that enables each prosumer of the
network to participate in energy trading with one another and the grid. This
poses a significant challenge in terms of modeling the decision-making process
of each participant with conflicting interest and motivating prosumers to
participate in energy trading and to cooperate, if necessary, for achieving
different energy management goals. Therefore, such decision-making process
needs to be built on solid mathematical and signal processing tools that can
ensure an efficient operation of the smart grid. This paper provides an
overview of the use of game theoretic approaches for P2P energy trading as a
feasible and effective means of energy management. As such, we discuss various
games and auction theoretic approaches by following a systematic classification
to provide information on the importance of game theory for smart energy
research. Then, the paper focuses on the P2P energy trading describing its key
features and giving an introduction to an existing P2P testbed. Further, the
paper zooms into the detail of some specific game and auction theoretic models
that have recently been used in P2P energy trading and discusses some important
finding of these schemes.Comment: 38 pages, single column, double spac
A Game-Theoretic Approach to Energy Trading in the Smart Grid
Electric storage units constitute a key element in the emerging smart grid
system. In this paper, the interactions and energy trading decisions of a
number of geographically distributed storage units are studied using a novel
framework based on game theory. In particular, a noncooperative game is
formulated between storage units, such as PHEVs, or an array of batteries that
are trading their stored energy. Here, each storage unit's owner can decide on
the maximum amount of energy to sell in a local market so as to maximize a
utility that reflects the tradeoff between the revenues from energy trading and
the accompanying costs. Then in this energy exchange market between the storage
units and the smart grid elements, the price at which energy is traded is
determined via an auction mechanism. The game is shown to admit at least one
Nash equilibrium and a novel proposed algorithm that is guaranteed to reach
such an equilibrium point is proposed. Simulation results show that the
proposed approach yields significant performance improvements, in terms of the
average utility per storage unit, reaching up to 130.2% compared to a
conventional greedy approach.Comment: 11 pages, 11 figures, journa
Leverage-induced systemic risk under Basle II and other credit risk policies
We use a simple agent based model of value investors in financial markets to
test three credit regulation policies. The first is the unregulated case, which
only imposes limits on maximum leverage. The second is Basle II and the third
is a hypothetical alternative in which banks perfectly hedge all of their
leverage-induced risk with options. When compared to the unregulated case both
Basle II and the perfect hedge policy reduce the risk of default when leverage
is low but increase it when leverage is high. This is because both regulation
policies increase the amount of synchronized buying and selling needed to
achieve deleveraging, which can destabilize the market. None of these policies
are optimal for everyone: Risk neutral investors prefer the unregulated case
with low maximum leverage, banks prefer the perfect hedge policy, and fund
managers prefer the unregulated case with high maximum leverage. No one prefers
Basle II.Comment: 27 pages, 8 figure
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