The More You See, the Less You Get: Price-Competing Insiders under Different Trading Mechanisms

The Prisoners’ Dilemma in collaborative carriers’ request selection

Central European Journal of Operations Research ◽

10.1007/s10100-020-00717-2 ◽

2021 ◽

Vol 29 (1) ◽

pp. 73-87 ◽

Cited By ~ 1

Author(s):

Margaretha Gansterer ◽

Richard F. Hartl

Keyword(s):

Computational Study ◽

Logistics Industry ◽

Individual Outcomes ◽

Selection Strategies ◽

Prisoners Dilemma ◽

Trading Mechanisms ◽

And Behavior ◽

The Impact ◽

Idle Capacity

AbstractLogistics providers have to utilize available capacities efficiently in order to cope with increasing competition and desired quality of service. One possibility to reduce idle capacity is to build coalitions with other players on the market. While the willingness to enter such coalitions does exist in the logistics industry, the success of collaborations strongly depends on mutual trust and behavior of participants. Hence, a proper mechanism design, where carriers do not have incentives to deviate from jointly established rules, is needed. We propose to use a combinatorial auction system, for which several properties are already well researched but little is known about the auction’s first phase, where carriers have to decide on the set of requests offered to the auction. Profitable selection strategies, aiming at maximization of total collaboration gains, do exist. However, the impact on individual outcomes, if one or more players deviate from jointly agreed selection rules is yet to be researched. We analyze whether participants in an auction-based transport collaboration face a Prisoners’ Dilemma. While it is possible to construct such a setting, our computational study reveals that carriers do not profit from declining the cooperative strategy. This is an important and insightful finding, since it further strengthens the practical applicability of auction-based trading mechanisms in collaborative transportation.

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The Paradigm Revolution in the Distribution Grid: The Cutting-Edge and Enabling Technologies

Open Computer Science ◽

10.1515/comp-2020-0202 ◽

2020 ◽

Vol 10 (1) ◽

pp. 369-395

Author(s):

C M Thasnimol ◽

R. Rajathy

Keyword(s):

Data Storage ◽

Renewable Energy Sources ◽

Distribution Network ◽

Phasor Measurement Units ◽

Self Healing ◽

Multi Agent Systems ◽

Directional Information ◽

Phasor Measurement ◽

Measurement Units ◽

Trading Mechanisms

AbstractBi-directional information and energy flow, renewable energy sources, battery energy storage, electric vehicle, self-healing capability, and demand response programs, etc., revolutionized the traditional distribution network into the smart distribution network. Adoption of modern technologies like intelligent meters such as advanced metering infrastructure & Micro-phasor measurement units, data storage, and analysis techniques and incentive-based electricity trading mechanisms can bring this paradigm shift. This study presents an overview of popular technologies that facilitate this transformation, giving focus on some prime technologies such as real-time monitoring based on Micro-phasor measurement units, data storage and analytics, blockchain technology, multi-agent systems, and incentive-based energy trading mechanisms

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Comments on the Market Crash: Six Months After

The Journal of Economic Perspectives ◽

10.1257/jep.2.3.45 ◽

1988 ◽

Vol 2 (3) ◽

pp. 45-50 ◽

Cited By ~ 43

Author(s):

Hayne Leland ◽

Mark Rubinstein

Keyword(s):

Standard Model ◽

Real Life ◽

The Other ◽

Financial Equilibrium ◽

The Standard Model ◽

Trading Mechanisms

Six months after the market crash of October 1987, we are still sifting through the debris searching for its cause. Two theories of the crash sound plausible -- one based on a market panic and the other based on large trader transactions -- though there is other evidence that is difficult to reconcile. If we are to believe the market panic theory or the Brady Commission's theory that the crash was primarily caused by a few large traders, we must strongly reject the standard model. We need to build models of financial equilibrium which are more sensitive to real life trading mechanisms, which account more realistically for the formation of expectations, and which recognize that, at any one time, there is a limited pool of investors available with the ability to evaluate stocks and take appropriate action in the market.

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