Incentive Mechanisms for Internet Congestion Management: Fixed-Budget Rebate versus Time-of-Day Pricing

Mobile data traffic has been steadily rising in the past years. This has generated a significant interest in the deployment of incentive mechanisms to reduce peak-time congestion. Typically, the design of these mechanisms requires information about user demand and sensitivity to prices. Such information is naturally imperfect. In this paper, we propose a \emph{fixed-budget rebate mechanism} that gives each user a reward proportional to his percentage contribution to the aggregate reduction in peak time demand. For comparison, we also study a time-of-day pricing mechanism that gives each user a fixed reward per unit reduction of his peak-time demand. To evaluate the two mechanisms, we introduce a game-theoretic model that captures the \emph{public good} nature of decongestion. For each mechanism, we demonstrate that the socially optimal level of decongestion is achievable for a specific choice of the mechanism's parameter. We then investigate how imperfect information about user demand affects the mechanisms' effectiveness. From our results, the fixed-budget rebate pricing is more robust when the users' sensitivity to congestion is "sufficiently" convex. This feature of the fixed-budget rebate mechanism is attractive for many situations of interest and is driven by its closed-loop property, i.e., the unit reward decreases as the peak-time demand decreases.Comment: To appear in IEEE/ACM Transactions on Networkin

Associative learning in baboons and humans: Species differences in learned attention to visual features

We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features

Prizes and Lemons: Procurement of Innovation under Imperfect Commitment

The literature on R&D contests implicitly assumes that contestants submit their innovation regardless of its value. This ignores a potential adverse selection problem. The present paper analyzes the procurement of innovations when the procurer cannot commit to never bargain with innovators who bypass the contest. We compare ?xed-prize tournaments with and without entry fees, and optimal scoring auctions with and without minimum score requirement. Our main result is that the optimal ?xed-prize tournament is more pro?table than the optimal auction since preventing bypass is more costly in the optimal auction

Externality-correcting taxes and regulation

Much of the literature on externalities has considered taxes and direct regulation as alternative policy instruments. Both instruments may in practice be imperfect, reflecting informational deficiencies and other limitations. We analyse the use of taxes and regulation in combination, to control externalities arising from individual consumption behaviour. We consider cases where taxes are either imperfectly differentiated to reflect individual differences in externalities, or where some consumption escapes taxation. In both cases we characterise the optimal instrument mix, and show how changing the level of direct regulation alters the optimal externality tax

Managing imperfect competition by pay for performance and reference pricing

I study a managed health service market where differentiated providers compete for consumers by choosing multiple service qualities, and where copayments that consumers pay and payments that providers receive for services are set by a payer. The optimal regulation scheme is two-sided. On the demand side, it justifies and clarifies value-based reference pricing. On the supply side, it prescribes pay for performance when consumers misperceive service benefits or providers have intrinsic quality incentives. The optimal bonuses are expressed in terms of demand elasticities, service technology, and provider characteristics. However, pay for performance may not outperform prospective payment when consumers are rational and providers are profit maximizing, or when one of the service qualities is not contractible

Barriers to industrial energy efficiency: a literature review

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Why Are People's Decisions Sometimes Worse with Computer Support?

In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature

A role for non-B DNA forming sequences in mediating microlesions causing human inherited disease

Missense/nonsense mutations and micro-deletions/micro-insertions of <21bp together represent ~76% of all mutations causing human inherited disease. Previous studies have shown that their occurrence is influenced by sequences capable of non-B DNA formation (direct, inverted and mirror repeats; G-quartets). We found that a greater than expected proportion (~21%) of both micro-deletions and micro-insertions occur within direct repeats and are explicable by slipped misalignment. A novel mutational mechanism, non-B DNA triplex formation followed by DNA repair, is proposed to explain ~5 % of micro-deletions and micro-insertions at mirror repeats. Further, G-quadruplex-forming sequences, direct and inverted repeats appear to play a prominent role in mediating missense mutations, whereas only direct and inverted repeats mediate nonsense mutations. We suggest a mutational mechanism involving slipped strand mispairing, slipped structure formation and DNA repair, to explain ~15% of missense and ~12% of nonsense mutations leading to the formation of perfect direct repeat s from imperfect repeats, or to the extension of existing direct repeats. Similar proportions of missense and nonsense mutations were explicable by the mechanism of hairpin loop formation and DNA repair leading to the formation of perfect inverted repeats from imperfect repeats. The proposed mechanisms provide new insights into mutagenesis underlying pathogenic micro-lesions

Pricing and Investments in Internet Security: A Cyber-Insurance Perspective

Internet users such as individuals and organizations are subject to different types of epidemic risks such as worms, viruses, spams, and botnets. To reduce the probability of risk, an Internet user generally invests in traditional security mechanisms like anti-virus and anti-spam software, sometimes also known as self-defense mechanisms. However, such software does not completely eliminate risk. Recent works have considered the problem of residual risk elimination by proposing the idea of cyber-insurance. In this regard, an important research problem is the analysis of optimal user self-defense investments and cyber-insurance contracts under the Internet environment. In this paper, we investigate two problems and their relationship: 1) analyzing optimal self-defense investments in the Internet, under optimal cyber-insurance coverage, where optimality is an insurer objective and 2) designing optimal cyber-insurance contracts for Internet users, where a contract is a (premium, coverage) pair