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The increase in online sales of living organisms has raised considerable concerns about its impact on wildlife. High demand for specific biological traits can intensify collection pressure via wildlife trades, yet the effects of colour variations and their geographical distribution on regional collection pressure remain poorly understood. This study analysed 11 years of transaction data — including, number, volume, price, date, colour types (DB: dark brown; RD: red; SB: sky blue; OT: other colours; and Mix) and collection locality — for the Geothelphusa dehaani species complex, renowned for its regional colour variations and aquarium popularity. Contrary to expectations that rarer colouration types (SB and OT types) are collected in higher numbers, the wild G. dehaani species complex collection pressure via online auctions (i.e. selling volume of wild-caught individuals) was higher for the common DB type (15,493 individuals) than for the rarer SB and OT types (2,073 and 173 individuals, respectively). This difference arose from a mechanism based on two distinct strategies linked to the colour traits and geographical distribution: the DB type followed a low-profit, high-sales (LPHS) strategy, selling large quantities at low unit prices, while the SB and OT types employed a high-profit, low-sales (HPLS) strategy, selling fewer individuals at higher unit prices. These findings highlight how colour variations influence online auction sales strategies and may negatively impact wildlife populations due to heterogeneous collection pressure at regional or local levels. Given the potential for rapid shifts of such mechanism driven by the appearance of mass sellers or the taxonomic revisions (i.e. additional value by a new species), our study suggests that implementing nuanced regulatory measures — for example, annual limits on the number of living animal auctions per taxon per year — could mitigate the risks associated with the diverse collection pressures through online auctions.

The paper presents a complete information model of bidding in second price sealed-bid and ascending-bid (English) auctions, in which potential buyers know the unit valuation of other bidders and may spitefully prefer that their rivals earn a lower surplus. Bidders with spiteful preferences should overbid in equilibrium when they know their rival has a higher value than their own, and bidders with a higher value underbid to reciprocate the spiteful overbidding of the lower value bidders. The model also predicts different bidding behavior in second price as compared to ascending-bid auctions. The paper also presents experimental evidence broadly consistent with the model. In the complete information environment, lower value bidders overbid more than higher value bidders, and they overbid more frequently in the second price auction than in the ascending price auction. Overall, the lower value bidder submits bids that exceed value about half the time. These patterns are not found in the incomplete information environment, consistent with the model.

Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory, there is little research on using blockchain in carbon emission trading schemes (ETS). This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology. Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security. Considering the augmentation of information interaction during the auction process brought about by blockchain, the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction. In particular, implementing smart contracts can provide a swift and automatic settlement. The simulation results of the proposed system demonstrate the following: (1) fine-grained access is possible with a second delay; (2) the average annual compliance levels increase by 2% when bidders’ learning behavior is considered; and (3) the blockchain network can process more than 350 reading operations or 7 writing operations in a second. Highlights Novel cooperative management of an ETS platform based on blockchain is proposed. The data access control policy based on CP-ABE is used to solve the contradiction between data privacy on the firm chain and government supervision. A learned auction strategy is proposed to suit the enhancement of information interaction caused by blockchain technology. This study provides a new method for climate change policymakers to consider the blockchain application of the carbon market.

Cloud providers offer various purchasing options to enable users to tailor their costs according to their specific requirements, including on-demand, reserved instances, and spot instances. On-demand and spot instances satisfy short-term workloads, whereas reserved instances fulfill long-term instances. However, there are workloads that fall outside of either long-term or short-term categories. Consequently, there is a notable absence of services specifically tailored for medium-term workloads. On-demand services, while offering flexibility, often come with high costs. Spot instances, though cost-effective, carry the risk of termination. Reserved instances, while stable and less expensive, may have a remaining period that extends beyond the duration of users’ tasks. This gap underscores the need for solutions that address the unique requirements and challenges associated with medium-term workloads in the cloud computing landscape. This paper introduces a new cloud broker that introduces IaaS services for medium-term workloads. On one hand, this broker strategically reserves resources from providers, and on the other hand, it interacts with users. Its interaction with users is twofold. It collects users’ preferences regarding commitment term for medium-term workloads and then transforms the leased resources based on commitment term, aligning with the requirements of most users. To ensure profitability, the broker sells these services utilizing an auction algorithm. Hence, in this paper, an auction algorithm is introduced and developed, which treats cloud services as virtual assets and integrates the depreciation over time. The findings affirm the lack of services that fulfill medium workloads while ensuring the financial viabilty and profitability of the broker, given that the estimated return on investment (ROI) is acceptable.

Abstract Traditional manual search for large-scale unknown areas is inefficient and hazardous, making multi-UAV (unmanned aerial vehicle) collaborative search a growing trend. However, existing methods still have shortcomings in complex environments or during sudden failures. This paper proposes a multi-UAV adaptive cooperative coverage search method based on area dynamic sensing. First, the search problem is modelled as an optimization problem, and a sensing set segmentation method is introduced along with performance metrics. Secondly, perception partitioning is used to avoid redundant detection, and dynamic path guidance technology drives the UAV swarm toward unexplored areas. Finally, an auction allocation task strategy is employed to plan optimal paths for each UAV in real-time, and collision avoidance algorithms are improved to enhance safety. Experiments demonstrate that this method excels in coverage rate, interference resistance, and environmental adaptability. Graphical Abstract Graphical Abstract Multi-UAV cooperative coverage search

For hydropower plants (HPPs) located in the second price zone of the Russian wholesale electric power and capacity market, the problem of optimizing expected profit in the capacity market is considered under the assumption that the HPPs will continue operating regardless of the results of the competitive capacity auction, under conditions of uncertainty regarding future water usage regimes and the presence of other random factors that reduce the volume of capacity sales. It is shown that if the probability distributions of the relevant random variables are known to the producer, the set of optimal strategies for HPPs in the competitive capacity auction is non-empty and is determined by the quantiles of these distributions. The results of the analysis are applied to practically significant cases.

eBay is the most successful of the many consumer-related on-line auction sites on the World Wide Web. Single-item auctions on eBay share characteristics with both English auctions and a hybrid combination of first-price and second-price sealed-bid auctions. Auctions on eBay allow bidders to choose between two different methods of bidding. A field study was conducted to determine the effects of bidding strategy on revenue realized in auctions conducted under the eBay market structure. The research draws on traditional auction theory derived from an examination of \"live\" auctions not mediated by computers. The results indicate differences between predicted and observed behavior. Two groups of bidders pay \"insurance\" premiums to ensure that their bidding is successful. These findings should generalize to other auction Web sites with similar market structures and have implications for bidders, sellers, and managers of auction Web sites.

We study learning in a dynamic setting where identical copies of a good are sold over time through a sequence of second-price auctions. Each agent in the market has an unknown independent private valuation that determines the distribution of the reward she obtains from the good; for example, in sponsored search settings, advertisers may initially be unsure of the value of a click. Though the induced dynamic game is complex, we simplify analysis of the market using an approximation methodology known as mean field equilibrium (MFE). The methodology assumes that agents optimize only with respect to long-run average estimates of the distribution of other players' bids. We show a remarkable fact: In a mean field equilibrium, the agent has an optimal strategy where she bids truthfully according to a conjoint valuation . The conjoint valuation is the sum of her current expected valuation, together with an overbid amount that is exactly the expected marginal benefit of one additional observation about her true private valuation. Under mild conditions on the model, we show that an MFE exists, and that it is a good approximation to a rational agent’s behavior as the number of agents increases. Formally, if every agent except one follows the MFE strategy, then the remaining agent’s loss on playing the MFE strategy converges to zero as the number of agents in the market increases. We conclude by discussing the implications of the auction format and design on the auctioneer’s revenue. In particular, we establish the revenue equivalence of standard auctions in dynamic mean field settings, and discuss optimal selection of reserve prices in dynamic auctions. This paper was accepted by Assaf Zeevi, stochastic models and simulation.

Consider an extensive-form mechanism, run by an auctioneer who communicates sequentially and privately with bidders. Suppose the auctioneer can deviate from the rules provided that no single bidder detects the deviation. A mechanism is credible if it is incentive-compatible for the auctioneer to follow the rules. We study the optimal auctions in which only winners pay, under symmetric independent private values. The first-price auction is the unique credible static mechanism. The ascending auction is the unique credible strategy-proof mechanism.

We propose a criterion of approximate incentive compatibility, strategy-proofness in the large (SP-L), and argue that it is a useful second-best to exact strategy-proofness (SP) for market design. Conceptually, SP-L requires that an agent who regards a mechanism’s “prices” as exogenous to her report—be they traditional prices as in an auction mechanism, or price-like statistics in an assignment or matching mechanism—has a dominant strategy to report truthfully. Mathematically, SP-L weakens SP in two ways: (1) truth-telling is required to be approximately optimal (within epsilon in a large enough market) rather than exactly optimal, and (2) incentive compatibility is evaluated ex interim, with respect to all full-support i.i.d. probability distributions of play, rather than ex post with respect to all possible realizations of play. This places SP-L in between the traditional notion of approximate SP, which evaluates incentives to manipulate ex post and as a result is too strong to obtain our main results in support of SP-L, and the traditional notion of approximate Bayes-Nash incentive compatibility, which, like SP-L, evaluates incentives to manipulate ex interim, but which imposes common knowledge and strategic sophistication assumptions that are often viewed as unrealistic.