Auction Rounds Research Articles

Are non-farming consumers willing to pay “a good market price” for iron-biofortified finger millet? Evidence from experimental auctions in Karnataka, India

PurposeBiofortification of staple crops is a promising strategy to alleviate micronutrient deficiencies in rural populations of the developing world. The possibility to sell biofortified crops at “a good market price” plays a vital role for the acceptance by smallholder farmers. This study is therefore focused on non-farming consumers' willingness to pay (WTP) for biofortified crops.Design/methodology/approachSpecifically, we elicited non-farming consumers' WTP a premium for the improved iron content (+30% iron) in a 1kg finger millet bag using a 2nd price Vickrey auction with six auction rounds and one health- and one process-related information treatment. Due to multiple bids per subject, premiums were analyzed using a linear mixed-effects model, controlling for market feedback and auction round.FindingsDespite more than half of the respondents being skeptical toward new crop varieties, the acceptance rate was very high (98% with a WTP above zero). The average premium amounted to 27% and could be significantly increased with the provision of health-related information. In contrast, information about the breeding method was ineffective. The WTP was significantly higher for higher income and lower for higher age, education and skepticism toward new crop varieties and increased with increasing rounds.Research limitations/implicationsOur results suggest that non-farming consumers are willing to pay “a good market price” for iron-biofortified finger millet. Our analysis also confirms the importance of health-related information for raising consumers' WTP. This information supports the further development and introduction of biofortified crops to alleviate micronutrient malnutrition.Originality/valueThis study adds to the still limited literature on consumers' WTP for iron-biofortified crops in India, focusing on non-farming consumers to assess the price such crops can achieve on the market.

Promoting Users’ Participation in Mobile Crowdsourcing: A Distributed Truthful Incentive Mechanism (DTIM) Approach

With the advancement of smartphones, mobile crowdsourcing has become a new computing paradigm to efficiently support novel mobile applications. Achieving good service quality of these applications, however, necessitates the participation of large number of smartphones, which can be obtained via providing suitable incentives to smartphone users. Nonetheless, most existing incentive mechanisms assume a centralized platform for recruiting smartphones, which is prone to expose the privacy of both smartphones and task requesters. In this context, this article studies a distributed truthful incentive mechanism (DTIM) for mobile crowdsourcing, where multiple auction rounds can be conducted locally in each smartphone and task requester. Specifically, in each auction round, the participating smartphones act as the sellers and submit bids to compete for their intended crowdsourcing tasks. The task requesters, on the other hand, act as the buyers that decide on the sellers and corresponding payments, depending on their submitted bids. Finally, based on the offered payment, each smartphone selects a buyer for trading to optimize its utility. It is shown that the proposed incentive mechanism is strategy-proof, budget balanced, individually rational, and computationally efficient. Numerical results provided corroborate the beneficial properties of the scheme.

Use of auctions to assess consumer value for fresh and end-of-code milk

The objective of this study was to survey consumers' milk purchasing behavior and investigate the effect of sensory experiences and an educational message on the perceived value of fluid milk at the beginning and near the end of code. Eleven auction sessions were carried out (n = 100 consumers), which included an explanation of the experiment, a survey about demographics and milk purchasing and consumption, sensory evaluation, an educational message, and 3 rounds of nth price auctions. Consumers were blindly served 2 pairs of milk samples from white-pigmented high-density polyethylene [2% and skim milk within 2 to 3 d of production (fresh) and 2% and skim milk with 2 to 3 d until the end of code (near-end)], and asked to indicate their preference and the level of acceptability for each sample using a 7-point hedonic scale. All samples were simultaneously evaluated by a panel of 8 judges who were trained to evaluate milk quality attributes on a 15 cm unstructured line scale. Results from the consumer panel acceptability rating session, trained panel descriptive analysis, and consumer auction bids were analyzed using multivariate factor analysis of variance. Subjecting pre- and post-survey responses to k means cluster analysis revealed 4 bidding populations in each round. Most participants (82%) indicated that they check the code date on milk every time they shop; 77% said they reached for the code date that was farthest out every time. However, on blind tasting, consumers did not prefer fresh over near-end milk. These findings were in agreement with their acceptability scores for fresh 2% (5.0/7.0), near-end 2% (5.2/7.0), fresh skim (4.5/7.0), and near-end skim (4.6/7.0) milks. Trained panelists did not detect a difference in "lacks freshness" flavor in fresh skim (1.9 cm/15.0 cm) or near-end skim milk (1.3 cm). Surprisingly, trained panelists did detect higher "lacks freshness" flavor in 1 lot of fresh 2% (2.3 cm) compared with near-end 2% milk (0.3 cm). When consumers bid on half gallons of milk with visible code dates, fresh skim was valued $0.27 higher than near-end skim, and fresh 2% was valued $0.29 higher than near-end 2%. After blind sensory evaluation, the margin between the fresh and near-end bids decreased to almost zero (fresh skim was valued only $0.03 more than near-end skim; near-end 2% was valued $0.01 more than fresh 2%). After the educational message about the meaning of code dates, consumer bids for near-end ($0.63) and fresh milk ($0.81) decreased compared with the first round of bidding ($0.74 and $1.01, respectively). Additionally, the margin in bids for fresh milk remained numerically higher than those for near-end milk ($0.15 for 2% and $0.21 for skim). The educational message about code date did not have the intended result of increasing consumer value for milk. Although consumers go out of their way to buy the freshest milk, they cannot necessarily distinguish fresh milk from milk at the end of code; consumers appear to value a code date that was farther out more than superior taste, even after a sensory experience and educational message.

Overbidding and matching rules in second-price auctions: An experimental study

This paper examines how matching conditions – stranger, partner, and group – in the second-price auction (SPA) affect the extent to which spite contributes to overbidding. In my theoretical framework, bidders experience negative spillovers from rivals’ payoffs and also a joy-of-winning (JOW). I derive maximum likelihood estimates of the spite and JOW parameters using 80% of the data, and find that the weights assigned to rivals’ payoffs in subjects’ utility functions are 8% and 15% in partner and group treatments respectively. Next, I evaluate the fits of the pure spite, pure JOW, and the combined models in the estimation and prediction sample, i.e., in the remaining 20% of subjects. Consistent with the estimation results, the goodness-of-fit statistics suggest that spite is more important in driving overbidding when bidders are matched against each other repeatedly. Further, I show that overbidding increases more in the partner treatment relative to the stranger treatment in the middle rounds of auctions. Finally, by eliciting subjects’ willingness-to-pay to harm others, I am able to link negative other-regarding preferences to overbidding and estimates of spitefulness in the SPA. This affirms the hypothesis that social motivation influences bidding behaviour in auctions with repeated interactions.

No-Regret Learning from Partially Observed Data in Repeated Auctions

We study a general class of repeated auctions, such as the ones found in electricity markets, as multi-agent games between the bidders. In such a repeated setting, bidders can adapt their strategies online using no-regret algorithms based on the data observed in the previous auction rounds. Well-studied no-regret algorithms depend on the feedback information available at every round, and can be mainly distinguished as bandit (or payoff-based), and full-information. However, the information structure found in auctions lies in between these two models, since participants can often obtain partial observations of their utilities under different strategies. To this end, we modify existing bandit algorithms to exploit such additional information. Specifically, we utilize the feedback information that bidders can obtain when their bids are not accepted, and build a more accurate estimator of the utility vector. This results in improved regret guarantees compared to standard bandit algorithms. Moreover, we propose a heuristic method for auction settings where the proposed algorithm is not directly applicable. Finally, we demonstrate our findings on case studies based on realistic electricity market models.

Use of auctions to assess consumer value for milk from different packaging

The objective of this study was to evaluate the effect of a sensory experience and educational message on consumer value for fluid milk in different containers: translucent high-density polyethylene (HDPE), white-pigmented HDPE (light-block), and paperboard. We carried out 11 auction sessions (n = 100). Sessions included an explanation of the experiment process; a survey about demographics and milk purchasing and consumption behaviors; a sensory evaluation; an educational message; and 3 rounds of nth price auctions. Consumers were blindly served 2 pairs of milk samples-2% milk from paperboard and translucent HDPE, and skim milk from paperboard and translucent HDPE-and asked to indicate their preference and level of acceptability for each, using a 7-point hedonic scale. Consumers were asked to ascribe a monetary value to each sample. Consumers were also asked to taste and assess a value for 2% and skim milk from light-block HDPE. All milk samples were evaluated simultaneously by a panel of 9 judges who were trained to evaluate milk quality attributes on a 15 cm unstructured line scale. All milk was from the same industry source, processed and stored on the same timeline for each session. Results from the consumer panel on acceptability, trained panel descriptive analysis and consumer bids were analyzed using multi-factor ANOVA. Subjecting responses to pre- and post-surveys to k means cluster analysis revealed 4 bidding populations in each round. Trained panelists detected higher levels of light-oxidized off-flavor in skim milk from translucent HDPE (5.8 cm) than from light-block HDPE (2.42 cm) and in 2% milk from translucent HDPE (5.1 cm) than from paperboard (0.32 cm). Although 84% of participants regularly purchased milk in plastic containers, consumers' acceptability scores for milk of the same fat content but from different packaging were not different. However, consumers gave higher acceptability scores to 2% milk from paperboard (5.2/7.0) than skim milk from paperboard (4.7/7.0). We found no differences in consumer value (US$) for 2% or skim milk in paperboard or translucent HDPE. However, after consumers received an educational message and tasted "Certified Fresh Taste" samples, they were willing to pay more for 2% milk in paperboard ($0.31 more) or HDPE ($0.38 more) packaging with a "Certified Fresh Taste" seal and for skim milk in paperboard ($0.15 more) or HDPE ($0.21 more) packaging with a "Certified Fresh Taste" seal than for milk in packaging without such labeling. Although consumers could not detect a difference in the milk from different packages, they indicated that they would pay more for a "Certified Fresh Taste" seal after hearing about the effects of light on milk in different packaging, demonstrating the importance to consumers of external cues about freshness.

T-Tree: The Tokyo toolbox for large-scale combinatorial auction experiments

This paper provides a brief introduction to t-Tree, a general auction software package that implements single-unit auctions, Vickrey–Clarke–Groves auctions (“VCG auctions”), Simultaneous Multiple Round Auctions (“SMRA”), and Combinatorial Clock Auctions (“CCA”) used in transportation, logistics, procurements, and spectrum allocation. The critical contribution of t-Tree is that it can handle computational complexities of the winner determination problem in large-scale combinatorial auctions by natively implementing Zhong et al. (2004)’s combinatorial optimization algorithms. Thus researchers can study large-scale combinatorial auction mechanisms off the shelf using t-Tree.

An Empirical Analysis of Double Round Auctions

AbstractThis article analyzes auctions that can feature two bidding rounds for the sale of a single good. In the first round, the seller, after analyzing the received bids, may elect to have k bidders rebid. The highest bidder in the second round then acquires the asset at the highest bid price. We use a sample of 67 properties that sold through this auction process. The 40 hotels in this sample are matched to a control group of 157 hotel properties that were sold in the conventional manner in order to develop a hedonic model for “conventional” sale prices. Using this model, we find that the double round auction mechanism increases the seller's expected revenue significantly: Specifically, in our sample, we estimate that the expected value of a double round auction bid is 8.4% higher than the estimated price if the property were sold using traditional methods. In addition, we show that the average bid is not significantly different from the estimated sale price. We further find (controlling for property characteristics) that the average bid increases by 3.7% from the first to the second round.

Learning and the possibility of losing own money reduce overbidding: Delayed payment in experimental auctions

In this study, we designed a delayed payment mechanism in laboratory second price auctions (SPAs), under which subjects received a cash endowment two weeks after the experiment day and had to use their own money to pay the experimental losses (if any) on the experiment day. We compared the effect of delayed payment on overbidding in the induced value SPAs with the conventional “on-the-spot” payment mechanism where the subjects received an endowment on the experiment day, and the prepaid mechanism where the subjects received the endowment two weeks before the experiment day. Each auction was repeated for 20 rounds to provide sufficient learning opportunities to the bidders. Our results showed that bids converged to the corresponding values over auction rounds and overbidding was reduced by previous losses, consistently with the adaptive learning theory. Moreover, overbidding seems to depend significantly on bidders’ cash holding, and the magnitude of the payment treatment effects depends crucially on liquidity constraints. In the presence of liquidity constraints, both delayed and prepaid payment mechanisms reduced overbidding, while in the absence of liquidity constraints, only the delayed endowment mechanism reduced overbidding. Furthermore, when controlling the degree of liquidity constraints, subjects with higher GPAs were less likely to overbid and the delayed endowment mechanism significantly reduced overbidding compared to other payment mechanisms. These results suggest that overbidding in SPAs might be caused by bounded rationality and could be reduced by adaptive learning especially when overbidding becomes “truly” costly to subjects.

Designing cost-efficient payments for forest-based carbon sequestration: An auction-based modeling approach

Conservation auctions can reveal information about the opportunity costs of afforestation, which can be combined with the carbon sequestration benefits of land-use changes to design cost-efficient payment systems. Furthermore, the impact of integrating cost-benefit information to ensure efficiency gains or to ensure minimal efficiency losses in multi-round conservation auctions, where landowners learn to extract information rents, depends on the level of correlation between costs and benefits. Incorporating discriminatory-price auction theory, and an agent-based model, simulated data are used to examine the cost-efficiency of cost-ranked and cost-benefit-ranked auction-based payment designs for forest-based carbon storage for alternative levels of correlation between afforestation opportunity costs and carbon sequestration capacities in static and dynamic settings. Results show that the cost-benefit-ranked design is more cost-efficient than the cost-ranked design in a static setting even though the relative spatial heterogeneities of costs and benefits are identical. More importantly, the cost-efficiency of the cost-benefit-ranked design is generally robust to deterioration as bidders learn over repeated auction rounds, compared with the cost-ranked design, and more resistant to deterioration for some levels of correlation.

A double auction scheme of resource allocation with social ties and sentiment classification for Device-to-Device communications

Resource allocation is an important way to solve the problem of the lack of spectrum resources, and humanity plays a key role in the communication quality of Device-to-Device (D2D) on the licensed radio spectrum. On these bases, in this paper, a double auction scheme of resource allocation with social ties and sentiment classification for D2D communications, which combines the social and physical domains, is proposed. Specifically, the double auction scheme is successfully implemented by the general assignment game algorithm (GA-alg), in which D2D pairs and spectrum resources act as sellers and bidders in each auction round, respectively. Additionally, social ties between users are determined by interaction data and the Kullback–Leibler divergence language model based on sentiment classification. Finally, simulation results not only illustrate that the proposed double auction scheme can achieve an optimal solution with lower complexity, but also demonstrate the effect of social ties on D2D communications.

Dynamic Pricing for Carriers in Physical Internet with Peak Demand Forecasting

This paper investigates a dynamic pricing problem for less-than-truckload (LTL) carriers during several auction periods in Physical Internet (PI), in consideration of peak demand forecasting. PI can be considered as the interconnection of logistics networks via open PI-hubs, in which there are plenty of requests to be transported. These requests continuously arrive at different auction periods with various quantity. Carriers can bid for these requests through participating several rounds of auction. In a dynamic environment, a major problem for the carrier is how to decide the bidding price to maximize his profit. Besides, to make better decision, when determining the bidding price in current period, carrier should forecast the possible requests in next periods, especially facing with peak/off-peak time periods. This paper proposes a dynamic pricing model considering the forecasted quantity of requests in the next auction periods to optimize the bidding price and maximize the total profit. A numerical study is conducted to evaluate the model and study how the future requests influence the current pricing decision, including the influence to the bidding price and profit.

Information Transparency in Business-to-Business Auction Markets: The Role of Winner Identity Disclosure

One of the central issues in auction design is how much information should be disclosed to bidders. In this paper, we examine bidder's identity disclosure in sequential business-to-business (B2B) auctions. Specifically, we compare two information disclosure policies, one that publicly discloses winners’ identities (the status quo) and an alternative policy that conceals winners’ identities. Using a large-scale field experiment in the Dutch flower auction market, we find that concealing winners’ identities can significantly increase the average winning price and thereby raise the seller’s revenue. We further explore the underlying mechanism that drives the observed effect. The empirical analysis of bidding behavior in these auctions suggests that bidders tend to imitate some of their competitors who have won in previous rounds of auctions and shade their bids accordingly. Concealing winners’ identities can disrupt such imitation heuristic, which in turn mitigates the price-declining trend in sequential rounds. Our findings have important implications for the design of information disclosure policies in B2B auction markets. This paper was accepted by Chris Forman, information systems.

Quality-Aware Service Selection for Multi-Tenant Service Oriented Systems Based on Combinatorial Auction

Multi-tenant service-oriented systems (SOSs) have become a major software engineering paradigm in the cloud environment. Instead of serving a single end-user, a multi-tenant SOS provides multiple tenants with similar and yet customized functionalities and potentially different quality-of-service (QoS) values. Multiple tenants' differentiated multi-dimensional quality constraints for the SOS further complicates the NP-hard problem of quality-aware service selection. Existing quality aware service selection approaches suffer from poor success rates of finding a solution, especially in scenarios where tenants' quality constraints are stringent, due to the lack of systematic consideration of three critical issues: 1) the need to fulfil multiple tenants' differentiated quality constraints; 2) the competition among service providers; and 3) the complementarity between services. This paper proposes a novel approach called combinatorial auction-based service selection for multi-tenant SOSs (CASSMT) to support effective quality-aware service selection for multi-tenant SOSs. CASSMT allows service providers to bid for the services of an SOS expressively. Based on received bids (i.e., QoS offers), CASSMT attempts to find a solution that achieves the system developer's optimization goal while fulfilling all tenants' quality constraints for the SOS. When no solution can be found based on the current bids, service providers can improve their bids to increase their chances of winning, which in the meantime, increases the chances of finding a solution. The experimental results show that CASSMT outperforms representative approaches in the success rate of finding a solution and system optimality. Meanwhile, its efficiency, measured by the number of auction rounds and computation time, is demonstrated to be satisfactory in scenarios on different scales.

Much ado about extremes: An experimental test of the shaping effect of prices on preferences

AbstractThere is evidence of a shaping effect of market prices on subjects’ evaluations in repeated private value auctions. This evidence has been traditionally interpreted as a pure behavioural phenomenon in contexts where the only available information is the market price. In this paper, we enrich the subjects’ information set by providing in treatment groups additional information on extreme asks. Our main results show a shaping effect both in the control and in the treatments and a stronger effect of the extreme information that more than halves the effect of the market price on subjects’ asks. Differently from the existing literature, we find that the provision of additional information inactivates the well‐documented tendency of the market price to fall at each auction round as a consequence of over‐asking correction. We discuss different possible mechanisms underlying this key finding.

The Crash of a Policy Pilot to Legally Define Community Energy. Evidence from the German Auction Scheme

“Community energy” is a highly contested issue not only in the German energy transition governance but also in the recent legislative procedure to recast energy market legislation within the EU’s “Winter Package”. This paper analyses the effects of the privileges for “community energy actors” in the German auction scheme for on-shore wind energy. Those privileges aim to guarantee a level playing field for small actors and to enhance societal acceptance. The results of the first rounds of auctions did not merely reveal an acceptable level of losses due to recognized trade-offs between policy objectives. Instead, the results indicate a complete failure regarding all three objectives of the revised support scheme for renewables—controlled renewable energy expansion, actor plurality and cost efficiency. The paper discusses whether the policy motivations translate appropriately into legislation. It suggests differentiating clearly the economic risks for small actors from the added value that is attributed to community energy actors. De-coupling these—often mixed—motives behind the demands for actor plurality unveils policy approaches that more adequately fit with these distinct motives. The paper finally proposes rather to integrate the politically desired values into the bid evaluation criteria instead of granting privileges to specific actors that are assumed to provide these values.

The Brazilian experience with auctions for wind power: An assessment of project delays and potential mitigation measures

Renewable energy auctions are gaining popularity worldwide. However, practical experiences remain ambiguous. This holds especially true for on-time implementation rates. In Brazil, only 17% of the awarded capacity in the first ten auction rounds reached commercial operation within the set out deadline. In this article, we investigate the causes for these delays and discuss potential mitigation measures, based on expert interviews and extensive data analysis. The most severe delays were caused by expansion to the transmission grid. Delays in the supply of wind turbines also led to significant project setbacks. Other factors could be considered of minor relevance, but have led in individual cases to significant delays. These include factors related to project financing, project or environmental licenses, logistics, land-use conflicts and poor project management. Brazil effectively reduced delays caused by transmission grid expansion through amendments to the auction design. Our analysis also shows that options to further mitigate delays through an advanced auction design are limited, as they lead to conflicts with other energy policy objectives, e.g. local production of wind turbines, or because the delays are caused by factors beyond the control of the project developers.

Multi-unit renewables auctions for small markets - Designing the Danish multi-technology auction scheme

Abstract This paper investigates different criteria for the design of multi-unit renewable energy (RES) auctions in small markets. The multi-technology RES auctions which are to be implemented in Denmark in 2018 serve as an exemplary case for the assessment. Focus of the analysis is how setting the auction schedule and the auctioned volume per round impacts the auction outcomes, accounting for the particular challenges of small markets. Agent-based modelling of the Danish auctions scheme demonstrates that the Danish RES market provides sufficient competition to auction higher volumes and follow more ambitious expansion goals. Furthermore, with a fixed budget, it is more effective in terms of deployment achieved, to hold fewer auctions with a larger volume. A flexibility mechanism that allows up to 50% of the auction volume to be shifted between auction rounds to accommodate potential large-scale marginal bidders, proves to be a useful tool to increase deployment rates, without negatively affecting bid prices. Furthermore it was shown that at current cost levels, only onshore bidders would be awarded in the envisaged multi-technology scheme. Also, large-scale and multi-project bidders are likely to be most cost competitive - indicating that further measures to maintain diversity could be useful.

Randomization to treatment failure in experimental auctions: The value of data from training rounds

In the experimental auctions literature, it is common practice to train subjects, who are often unfamiliar with the auction procedure, by conducting a few training (often hypothetical) auctions. Data from these practice auctions are rarely reported in scientific papers. We argue that valuable information can be garnered by looking at data coming from the training rounds of experimental auctions. We provide evidence that if we had neglected insights gained from the training auction data, we would not have been able to detect a failure of randomization to treatment that rendered us biased estimates of the true causal effects due to unobserved heterogeneity. As a case study, we use data from an experiment that seeks to elaborate on the mediating role of mood states on projection bias. Following a mood induction procedure, subjects are found to bid more under negative mood (as compared to positive mood) for products that are delivered in the future but bid less under negative mood for products that are delivered in present time. We show that information from the training auction rounds render the observed effect a biased estimate, which is likely a consequence of the failure of randomization to treatment.

Synergistic valuations and efficiency in spectrum auctions

In spectrum auctions, bidders typically have synergistic values for combinations of licenses. This has been the key argument for the use of combinatorial auctions in the recent years. Considering synergistic valuations turns the allocation problem into a computationally hard optimization problem that generally cannot be approximated to a constant factor in polynomial time. Ascending auction designs such as the Simultaneous Multiple Round Auction (SMRA) and the single-stage or two-stage Combinatorial Clock Auction (CCA) can be seen as simple heuristic algorithms to solve this problem. Such heuristics do not necessarily compute the optimal solution, even if bidders are truthful. We study the average efficiency loss that can be attributed to the simplicity of the auction algorithm with different levels of synergies. Our simulations are based on realistic instances of bidder valuations we inferred from bid data from the 2014 Canadian 700 MHz auction. The goal of the paper is not to reproduce the results of the Canadian auction but rather to perform “out-of-sample” counterfactuals comparing SMRA and CCA under different synergy conditions when bidders maximize payoff in each round. With “linear” synergies, a bidder's marginal value for a license grows linearly with the total number of licenses won, while with the “extreme national” synergies, this marginal value is independent of the number of licenses won unless the bidder wins all licenses in a national package. We find that with the extreme national synergy model, the CCA is indeed more efficient than SMRA. However, for the more realistic case of linear synergies, SMRA outperforms various versions of CCA that have been implemented in the field including the one used in the Canadian 700 MHz auction. Overall, the efficiency loss of all ascending auction algorithms is small even with high synergies, which is remarkable given the simplicity of the algorithms.