2,111 research outputs found
Beyond myopic best response (in Cournot competition)
A Nash Equilibrium is a joint strategy profile at which each agent myopically plays a best response to the other agents' strategies, ignoring the possibility that deviating from the equilibrium could lead to an avalanche of successive changes by other agents. However, such changes could potentially be beneficial to the agent, creating incentive to act non-myopically, so as to take advantage of others' responses.
To study this phenomenon, we consider a non-myopic Cournot competition, where each firm selects whether it wants to maximize profit (as in the classical Cournot competition) or to maximize revenue (by masquerading as a firm with zero production costs).
The key observation is that profit may actually be higher when acting to maximize revenue, (1) which will depress market prices, (2) which will reduce the production of other firms, (3) which will gain market share for the revenue maximizing firm, (4) which will, overall, increase profits for the revenue maximizing firm. Implicit in this line of thought is that one might take other firms' responses into account when choosing a market strategy. The Nash Equilibria of the non-myopic Cournot competition capture this action/response issue appropriately, and this work is a step towards understanding the impact of such strategic manipulative play in markets.
We study the properties of Nash Equilibria of non-myopic Cournot competition with linear demand functions and show existence of pure Nash Equilibria, that simple best response dynamics will produce such an equilibrium, and that for some natural dynamics this convergence is within linear time. This is in contrast to the well known fact that best response dynamics need not converge in the standard myopic Cournot competition.
Furthermore, we compare the outcome of the non-myopic Cournot competition with that of the standard myopic Cournot competition. Not surprisingly, perhaps, prices in the non-myopic game are lower and the firms, in total, produce more and have a lower aggregate utility
Truly Online Paging with Locality of Reference
The competitive analysis fails to model locality of reference in the online
paging problem. To deal with it, Borodin et. al. introduced the access graph
model, which attempts to capture the locality of reference. However, the access
graph model has a number of troubling aspects. The access graph has to be known
in advance to the paging algorithm and the memory required to represent the
access graph itself may be very large.
In this paper we present truly online strongly competitive paging algorithms
in the access graph model that do not have any prior information on the access
sequence. We present both deterministic and randomized algorithms. The
algorithms need only O(k log n) bits of memory, where k is the number of page
slots available and n is the size of the virtual address space. I.e.,
asymptotically no more memory than needed to store the virtual address
translation table.
We also observe that our algorithms adapt themselves to temporal changes in
the locality of reference. We model temporal changes in the locality of
reference by extending the access graph model to the so called extended access
graph model, in which many vertices of the graph can correspond to the same
virtual page. We define a measure for the rate of change in the locality of
reference in G denoted by Delta(G). We then show our algorithms remain strongly
competitive as long as Delta(G) >= (1+ epsilon)k, and no truly online algorithm
can be strongly competitive on a class of extended access graphs that includes
all graphs G with Delta(G) >= k- o(k).Comment: 37 pages. Preliminary version appeared in FOCS '9
On-Line Paging against Adversarially Biased Random Inputs
In evaluating an algorithm, worst-case analysis can be overly pessimistic.
Average-case analysis can be overly optimistic. An intermediate approach is to
show that an algorithm does well on a broad class of input distributions.
Koutsoupias and Papadimitriou recently analyzed the least-recently-used (LRU)
paging strategy in this manner, analyzing its performance on an input sequence
generated by a so-called diffuse adversary -- one that must choose each request
probabilitistically so that no page is chosen with probability more than some
fixed epsilon>0. They showed that LRU achieves the optimal competitive ratio
(for deterministic on-line algorithms), but they didn't determine the actual
ratio.
In this paper we estimate the optimal ratios within roughly a factor of two
for both deterministic strategies (e.g. least-recently-used and
first-in-first-out) and randomized strategies. Around the threshold epsilon ~
1/k (where k is the cache size), the optimal ratios are both Theta(ln k). Below
the threshold the ratios tend rapidly to O(1). Above the threshold the ratio is
unchanged for randomized strategies but tends rapidly to Theta(k) for
deterministic ones.
We also give an alternate proof of the optimality of LRU.Comment: Conference version appeared in SODA '98 as "Bounding the Diffuse
Adversary
Makespan Minimization via Posted Prices
We consider job scheduling settings, with multiple machines, where jobs
arrive online and choose a machine selfishly so as to minimize their cost. Our
objective is the classic makespan minimization objective, which corresponds to
the completion time of the last job to complete. The incentives of the selfish
jobs may lead to poor performance. To reconcile the differing objectives, we
introduce posted machine prices. The selfish job seeks to minimize the sum of
its completion time on the machine and the posted price for the machine. Prices
may be static (i.e., set once and for all before any arrival) or dynamic (i.e.,
change over time), but they are determined only by the past, assuming nothing
about upcoming events. Obviously, such schemes are inherently truthful.
We consider the competitive ratio: the ratio between the makespan achievable
by the pricing scheme and that of the optimal algorithm. We give tight bounds
on the competitive ratio for both dynamic and static pricing schemes for
identical, restricted, related, and unrelated machine settings. Our main result
is a dynamic pricing scheme for related machines that gives a constant
competitive ratio, essentially matching the competitive ratio of online
algorithms for this setting. In contrast, dynamic pricing gives poor
performance for unrelated machines. This lower bound also exhibits a gap
between what can be achieved by pricing versus what can be achieved by online
algorithms
THE IMPACT OF THE CAP-AND-TRADE SYSTEM ON SUSTAINABLE DEVELOPMENT IN ROMANIA
The issue of pollution is an important contemporary issue. Within the last 20 years, there have been global studies on how to stop global warming. The European Union is the world leader in concrete measures undertaken in this respect. The introduction of the cap-and-trade system, begun in 2005 with the carbon emission certificate trade mechanism is considered a a modest succes that needs perfecting. The new EU directives of 2009, that come into effect in 2013, test member countries in the area of conventional and renewable energy strategies which have to be adapted to the national environmental protection strategies.market failure, cap-and-trade, emission certificates, carbon market
Oblivious Transfer based on Key Exchange
Key-exchange protocols have been overlooked as a possible means for
implementing oblivious transfer (OT). In this paper we present a protocol for
mutual exchange of secrets, 1-out-of-2 OT and coin flipping similar to
Diffie-Hellman protocol using the idea of obliviously exchanging encryption
keys. Since, Diffie-Hellman scheme is widely used, our protocol may provide a
useful alternative to the conventional methods for implementation of oblivious
transfer and a useful primitive in building larger cryptographic schemes.Comment: 10 page
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