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The Internet enables users to access vast resources, but it can also expose users to harmful cyber-attacks. It is imperative that users be informed about a security incident in a timely manner in order to make proper decisions. Visualization of security threats and warnings is one of the effective ways to inform users. However, visual cues are not always accessible to all users, and in particular, those with visual impairments. This late-breaking-work paper hypothesizes that the use of proper sounds in conjunction with visual cues can better represent security alerts to all users. Toward our research goal to validate this hypothesis, we first describe a methodology, referred to as sonification, to effectively design and develop auditory cyber-security threat indicators to warn users about cyber-attacks. Next, we present a case study, along with the results, of various types of usability testing conducted on a number of Internet users who are visually impaired. The presented concept can be viewed as a general framework for the creation and evaluation of human factor interactions with sounds in a cyber-space domain. The paper concludes with a discussion of future steps to enhance this work.
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Socioeconomic threats are more salient to farmers than environmental threats
- Award ID(s):
- 1735095
- PAR ID:
- 10294319
- Date Published:
- Journal Name:
- Journal of Rural Studies
- Volume:
- 86
- Issue:
- C
- ISSN:
- 0743-0167
- Page Range / eLocation ID:
- 508 to 517
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Meka, Raghu (Ed.)There has been substantial recent concern that automated pricing algorithms might learn to collude Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, in which sellers play strategies which threaten to punish their competitors if they ever defect from a set of supra-competitive prices, and these strategies can be automatically learned. But threats are anti-competitive on their face. In fact, a standard economic intuition is that supra-competitive prices emerge from either the use of threats, or a failure of one party to correctly optimize their payoff. Is this intuition correct? Would explicitly preventing threats in algorithmic decision-making prevent supra-competitive prices when sellers are optimizing for their own revenue? No. We show that supra-competitive prices can robustly emerge even when both players are using algorithms which do not explicitly encode threats, and which optimize for their own revenue. Since deploying an algorithm is a form of commitment, we study sequential Bertrand pricing games (and a continuous variant) in which a first mover deploys an algorithm and then a second mover optimizes within the resulting environment. We show that if the first mover deploys any algorithm with a no-regret guarantee, and then the second mover even approximately optimizes within this now static environment, monopoly-like prices arise. The result holds for any no-regret learning algorithm deployed by the first mover and for any pricing policy of the second mover that obtains them profit at least as high as a random pricing would - and hence the result applies even when the second mover is optimizing only within a space of non-responsive pricing distributions which are incapable of encoding threats. In fact, there exists a set of strategies, neither of which explicitly encode threats that form a Nash equilibrium of the simultaneous pricing game in algorithm space, and lead to near monopoly prices. This suggests that the definition of algorithmic collusion; may need to be expanded, to include strategies without explicitly encoded threats.more » « less
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There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, in which sellers play strategies which threaten to punish their competitors who refuse to support high prices, and these strategies can be automatically learned. In fact, a standard economic intuition is that supra-competitive prices emerge from either the use of threats, or a failure of one party to optimize their payoff. Is this intuition correct? Would preventing threats in algorithmic decision-making prevent supra-competitive prices when sellers are optimizing for their own revenue? No. We show that supra-competitive prices can emerge even when both players are using algorithms which do not encode threats, and which optimize for their own revenue. We study sequential pricing games in which a first mover deploys an algorithm and then a second mover optimizes within the resulting environment. We show that if the first mover deploys any algorithm with a no-regret guarantee, and then the second mover even approximately optimizes within this now static environment, monopoly-like prices arise. The result holds for any no-regret learning algorithm deployed by the first mover and for any pricing policy of the second mover that obtains them profit at least as high as a random pricing would -- and hence the result applies even when the second mover is optimizing only within a space of non-responsive pricing distributions which are incapable of encoding threats. In fact, there exists a set of strategies, neither of which explicitly encode threats that form a Nash equilibrium of the simultaneous pricing game in algorithm space, and lead to near monopoly prices. This suggests that the definition of ``algorithmic collusion'' may need to be expanded, to include strategies without explicitly encoded threats.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.jrurstud.2021.07.016
