This paper presents a multi‐criteria decision‐making approach for the selection of a sustainable product‐package design, accounting for the different actors within a food supply chain. The study extends the focus of sustainable packaging design to the collective of all supply chain actors. Decision criteria are identified via a literature review, and current product‐package alternatives are collected via interviews. With the inputs of these criteria and the alternative designs, a multi‐criteria decision‐making problem is formulated and solved using Best Worst Method (BWM). BWM finds the weights of the criteria. Using these weights, the ranking of the alternatives is found. The implementation of the analysis took place for three selected products of the Kraft Heinz Company. Data on the preferences of the supply chain members of these selected products were collected, and the optimal package designs were selected. It is shown through sensitivity analysis that modifying the weights that decision makers assign to the preferences of the supply chain members and the importance of the dimensions of sustainability have an effect on the selection of the optimal design.
This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly forecast of a future event (such as a meteorological phenomenon) or a probabilistic estimate of a specific parameter (such as the quality of an expected service), with a specified minimum precision, from one or more agents. In the first stage, the centre elicits the agents' true costs and identifies the agent that can provide an estimate of the specified precision at the lowest cost. Then, in the second stage, the centre uses an appropriately scaled strictly proper scoring rule to incentivise this agent to generate the estimate with the required precision, and to truthfully report it. In particular, this is the first mechanism that can be applied to settings in which the centre has no knowledge about the actual costs involved in the generation an agents' estimates and also has no external means of evaluating the quality and accuracy of the estimates it receives. En route to this mechanism, we first consider a setting in which any single agent can provide an estimate of the required precision, and the centre can evaluate this estimate by comparing it with the outcome which is observed at a later stage. This mechanism is then extended, so that it can be applied in a setting where the agents' different capabilities are reflected in the maximum precision of the estimates that they can provide, potentially requiring the centre to select multiple agents and combine their individual results in order to obtain an estimate of the required precision. For all three mechanisms (the original and the two extensions), we prove their economic properties (i.e. incentive compatibility and individual rationality) and then perform a number of numerical simulations. For the single agent mechanism we compare the quadratic, spherical and logarithmic scoring rules with a parametric family of scoring rules. We show that although the logarithmic scoring rule minimises both the mean and variance of the centre's total payments, using this rule means that an agent may face an unbounded penalty if it provides an estimate of extremely poor quality. We show that this is not the case for the parametric family, and thus, we suggest that the parametric scoring rule is the best candidate in our setting. Furthermore, we show that the 'multiple agent' extension describes a family of possible approaches to select agents in the first stage of our mechanism, and we show empirically and prove analytically that there is one approach that dominates all others. Finally, we compare our mechanism to the peer prediction mechanism introduced by Miller et al. (2007b) and show that the centre's total expected payment is the same in both mechanisms (and is equal to total expected payment in the case that the estimates can be compared to the actual outcome), while the variance in these payments is significantly reduced within our mechanism.
A major restructuring of electricity markets takes place worldwide, pursuing maximum economic efficiency. In most modern electricity markets, including the widely adapted Locational Marginal Price (LMP) market, efficiency is only guaranteed under the assumption of perfect competition. Moreover, market design is heavily focused on deterministic conventional generation. Electricity markets, though, are vulnerable to strategic behaviors and challenged by the increased penetration of renewable energy generation. In this paper, we cope with the aforementioned bottlenecks by investigating the application of Vickrey-Clarke-Groves (VCG) auction in a twostage stochastic electricity market. The VCG mechanism achieves incentive-compatibility by rewarding market participants for their contribution towards market efficiency, being attractive from both market operation and participants perspectives. Both traditional and VCG market-clearing approaches are explored and compared, investigating as well the impact of increasing wind power penetration. The main shortcoming of VCG, i.e., not ensuring revenue-adequacy, is quantified in terms of market budget imbalance for various levels of wind power penetration. To this end, a novel ex-post budget redistribution scheme is proposed, which achieves to partially recover budget deficit.
This article addresses two important issues in public procurement: ex ante uncertainty about the participating agents' qualities and costs and their strategic behaviour. We present a novel multi-dimensional auction that incentivises agents to make a partial inquiry into the procured task and to honestly report quality-cost probabilistic estimates based on which the principal can choose the agent that offers the best value for money. The mechanism extends second score auction design to settings where the quality is uncertain and it provides incentives to both collect information and deliver desired qualities.
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