211 research outputs found
The Price of Uncertainty: Chance-constrained OPF vs. In-hindsight OPF
The operation of power systems has become more challenging due to feed-in of
volatile renewable energy sources. Chance-constrained optimal power flow
(ccOPF) is one possibility to explicitly consider volatility via probabilistic
uncertainties resulting in mean-optimal feedback policies. These policies are
computed before knowledge of the realization of the uncertainty is available.
On the other hand, the hypothetical case of computing the power injections
knowing every realization beforehand---called in-hindsight OPF(hOPF)---cannot
be outperformed w.r.t. costs and constraint satisfaction. In this paper, we
investigate how ccOPF feedback relates to the full-information hOPF. To this
end, we introduce different dimensions of the price of uncertainty. Using mild
assumptions on the uncertainty we present sufficient conditions when ccOPF is
identical to hOPF. We suggest using the total variational distance of
probability densities to quantify the performance gap of hOPF and ccOPF.
Finally, we draw upon a tutorial example to illustrate our results.Comment: Accepted for publication at the 20th Power Systems Computation
Conference (PSCC) in Dublin, 201
The Price of Uncertainty in Present-Biased Planning
The tendency to overestimate immediate utility is a common cognitive bias. As a result people behave inconsistently over time and fail
to reach long-term goals. Behavioral economics tries to help affected individuals
by implementing external incentives. However, designing robust
incentives is often difficult due to imperfect knowledge of the parameter
β ∈ (0, 1] quantifying a person’s present bias. Using the graphical model
of Kleinberg and Oren [8], we approach this problem from an algorithmic
perspective. Based on the assumption that the only information about
β is its membership in some set B ⊂ (0, 1], we distinguish between two
models of uncertainty: one in which β is fixed and one in which it varies
over time. As our main result we show that the conceptual loss of effi-
ciency incurred by incentives in the form of penalty fees is at most 2
in the former and 1 + max B/ min B in the latter model. We also give
asymptotically matching lower bounds and approximation algorithms
The Price of Uncertain Priors in Source Coding
We consider the problem of one-way communication when the recipient does not
know exactly the distribution that the messages are drawn from, but has a
"prior" distribution that is known to be close to the source distribution, a
problem first considered by Juba et al. We consider the question of how much
longer the messages need to be in order to cope with the uncertainty about the
receiver's prior and the source distribution, respectively, as compared to the
standard source coding problem. We consider two variants of this uncertain
priors problem: the original setting of Juba et al. in which the receiver is
required to correctly recover the message with probability 1, and a setting
introduced by Haramaty and Sudan, in which the receiver is permitted to fail
with some probability . In both settings, we obtain lower bounds that
are tight up to logarithmically smaller terms. In the latter setting, we
furthermore present a variant of the coding scheme of Juba et al. with an
overhead of bits, thus also establishing the
nearly tight upper bound.Comment: To appear in IEEE Transactions on Information Theor
The price of uncertainty: kampung land politics in post-Suharto Bandung
Most Indonesian urban poor live in ramshackle settlements called kampungs and occupy land according to tenure arrangements unrecognised by the formal land law regime. Reform since the 1998 fall of Suharto has led to some recognition of these 'semiformal' arrangements. This complicates the ambitious development agenda of a city like Bandung, pitting two sides with seemingly conflicting interests against each other: the urban poor and the municipal government. Both are dissatisfied with Bandung's land reforms
Towards Realistic Threat Modeling: Attack Commodification, Irrelevant Vulnerabilities, and Unrealistic Assumptions
Current threat models typically consider all possible ways an attacker can
penetrate a system and assign probabilities to each path according to some
metric (e.g. time-to-compromise). In this paper we discuss how this view
hinders the realness of both technical (e.g. attack graphs) and strategic (e.g.
game theory) approaches of current threat modeling, and propose to steer away
by looking more carefully at attack characteristics and attacker environment.
We use a toy threat model for ICS attacks to show how a realistic view of
attack instances can emerge from a simple analysis of attack phases and
attacker limitations.Comment: Proceedings of the 2017 Workshop on Automated Decision Making for
Active Cyber Defens
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