Bid Values Research Articles

Supplier selection with rank reversal in public tenders

For supplier selection in the public sector, the Weighted Sum Model is often used in combination with relative scoring methods that allow rank reversal. With rank reversal we refer to a changed order in the ranking of bids leading to a new winner, after removing or adding a non-optimal bid that does not win the original tender. In practice, an important reason indicated by practitioners for using methods that allow rank reversal is that it would rarely occur in practice. Based on an analysis of 303 Dutch public tenders, this research shows this is not true. In about 1 out of 5 the tenders, rank reversal occurs after adding non-optimal fictional bids to tenders that do not have quality thresholds. After removing bids, the rate is about 1 out of 40 if a curved relative scoring method is used. In addition, the research shows that rank reversal rates increase when (i) there is no quality threshold, (ii) the number of bids increases, (iii) bid price variance increases, and (iv) price weights are not very low or high. We argue that relative scoring methods that allow rank reversal should not be used in public procurement, or otherwise only in exceptional cases, as it conflicts with public procurement principles and leads to reduced overall bid value.

Uncovering the willingness to pay for ecological red lines protection: Evidence from China

Ecological red line policy is an environmental policy that the national government aims to protect important ecosystems by dividing them into different functional red lines. Ecological red lines is the guarantee lines of ecological services and the bottom lines of ecological security. Quantifying the value of ecological red lines is the inherent requirement of the development of ecological red lines policy. This study investigated whether residents in Nanjing were willing to pay for ecological red lines protection by using the theoretical framework of the double-bounded dichotomous choice method. The survey collected a sample of 1,058 respondents, of whom 835 were willing to pay to protect the ecological red lines, and most of those unwilling to pay believed that protection of ecological red lines should be paid by governments or polluters. The calculated mean Willingness to Pay was 300.07 CNY·year−1, among which the respondents showed the highest willingness to pay for nature reserve areas and water resources reserve areas and the lowest willingness to pay for cultural landscape areas. The final estimated non-use value of ecological red lines in Nanjing was 2550 million CNY. By analyzing the results of the logistic regression model, education level and family annual income have a significant positive effect on Willingness to Pay, while age, the influence of ecological red lines on themselves, and the initial bid value are negatively significant. The evaluation results of this study have certain value mainly in the framework of sustainable development of ecological red lines, to improve residents’ awareness and participation in the protection of ecological red lines, and to provide scientific support for the management plan and policies to improve the environmental quality of ecological red lines.

Smallholder households' willingness to pay for conservation of ecosystem services of Altash National Park of Ethiopia

Despite exceptional ecosystem services are providing by Altash National Park, anthropogenic activities exert immense pressures on the park's natural resources from the surrounding community which is eventually degrading the productivity of ecosystem services. Therefore, this study investigated smallholder households' willingness to pay for conservation of ecosystem services of the Park from October 2019 to December 2020 using 196 sample respondents selected through systematic random sampling. Double bounded dichotomous choice followed by open ended format of contingent valuation method was used to state households' willingness to pay (WTP) and their maximum WTP respectively for conservation of ecosystem services of the park. Seemingly unrelated bivariate probit model was employed to analyze the data. First and second offered bid values, gender of respondent, years of living in kebelle, education status, distance from home to park, benefit of preservation, distance from farm to park, training on park conservation were found to have statistically significant influence on households' WTP for conservation of ecosystem services of the park. The expected aggregate WTP from double bounded dichotomous choice and open ended format was estimated 1,511,172.96 and 1,526,194.56 birr, respectively. Based on the findings, all respondents were willing to pay for the conservation of ecosystem services of the park. Therefore, for every decision and formulation of policies and strategies, participation of local communities should be considered. Finally, regular training and workshop should be delivered for local communities to enhance awareness about the environmental and economic values of the park and to develop sense of ownership.

Combinatorial Spectrum E-Auction for 5G Heterogeneous Networks: A Zether-Based Approach

5G heterogeneous network (HetNet) is a novel network topology that integrates various kinds of wireless access technologies such as 4G Long-Term Evolution (LTE), Wi-Fi, and so on. Despite greatly improving spectrum efficiency, it poses enormous challenges to spectrum e-auction. Firstly, due to high mobility, bidders may be interested in different spectrums in terms of time or geolocation. Secondly, one’s bidding value should be protected against rival bidders or adversaries to avoid vicious competition as well as privacy leakage. Thirdly, the ubiquitous HetNet requires a trustworthy distributed auction framework rather than a centralized auctioneer-based pattern. Aiming at overcoming these obstacles above, we proposed a blockchain-based combinatorial spectrum e-auction framework. Different from other blockchain-based solutions of using SGX to realize trust processing in the auction phase, we adopt Zether, a privacy-preserving smart contract, as the main building block. Besides, the bidding value is preserved from the beginning to the end, even though the time-consuming Paillier homomorphic encryption and garbled circuits are absent. We provide the auction security by leveraging Σ -Bullets, a zero-knowledge proof mechanism. Theoretical analysis and extensive evaluation also indicate that our approach is better than the state-of-the-art works in terms of efficiency and effectiveness.

Evaluating the German PV auction program: The secrets of individual bids revealed

Auctions have become the primary instrument for promoting renewable energy around the world. However, the data published on such auctions are typically limited to aggregated information (e.g., total awarded capacity, average payments). These data constraints hinder the evaluation of realisation rates and other relevant auction dynamics. In this study, we present an algorithm to overcome these data limitations in German renewable energy auction programme by combining publicly available information from four different databases. We apply it to the German solar auction programme and evaluate auctions using quantitative methods. We calculate realisation rates and—using correlation and regression analysis—explore the impact of PV module prices, competition, and project and developer characteristics on project realisation and bid values. Our results confirm that the German auctions were effective. We also found that project realisation took, on average, 1.5 years (with 28% of projects finished late and incurring a financial penalty), nearly half of projects changed location before completion (again, incurring a financial penalty) and small and inexperienced developers could successfully participate in auctions.

English

The aim of this paper is to estimate households’ willingness to pay (WTP) for the development of community based tourism (CBT) around Alelo Bad hot spring water. To achieve the stated objective, relevant data was collected from a total of 157 sample respondents through questionnaires, interviews and field observation techniques. In this study, both descriptive and econometric models (Seemingly Unrelated Bivariate Probit and Probit models) were used to analyze the collected data. The descriptive statistics clearly shows that 66.24% of the respondents were willing to pay for the existence of the proposed project while 33.76% were not willing to pay. The seemingly unrelated bivariate probit model output revealed that the mean WTP of the sample household heads per year for the proposed project was birr 468.75 ($16.401) as well as the total sample respondents has estimated to pay birr 73,593.75 ($2,575.045) per year per household head. Gender, age, family size, off-farm income, education status, social responsibility and initial bid values were statistically significant variables which affect the WTP decision of household heads in the study area. The government should create a conducive environment for different stakeholders to actively engage in boosting the returns of the tourism sector and fulfillment of the proposed project in the study area. The tourism policy should be amended in the form of allocating higher proportion of the revenue obtained from the tourism sector to the local communities for the development of social infrastructure, improving the living standard of the local communities and sustaining resource conservation.   Key words: Alelo bad hot spring water, WTP, seemingly unrelated bivariate probit and probit models, Ethiopia.

Promoting Green Real Estate Development by Increasing Residents’ Satisfaction

Green real estate is a new form of development that seeks to reduce the negative impact of real estate construction on the environment and society. It offers various forms of environmental and social utility and also bears higher construction costs. Residents’ satisfaction and willingness to pay (WTP) are therefore of great significance for the progress of green real estate. However, residents’ satisfaction with green residential areas and its correlation with WTP has been insufficiently researched. This study draws on face-to-face interviews conducted with 614 households in Qingtangwan (an exemplary green real estate project in Beijing) to extract information about residents’ satisfaction and WTP. With regard to satisfaction, it identifies five main components, specifically the operation and maintenance of residential areas, indoor and outdoor comfort, building quality, sustainable community attachment, and public facility accessibility (this last category had lower satisfaction). In terms of WTP, residents’ mean WTP was found to be CHY 204.23 per month, which is approximately USD 31.19. In addition to the bid value, the indoor and outdoor comfort, building quality, sense of community, and public facility accessibility were all found to be significantly related to residents’ WTP. Relevant policy recommendations for promoting this development mode included the establishment of public funds, the regular collection and disclosure of information about residents’ satisfaction, phased rent increases, and the enhancement of community cultural construction.

Stock liquidity, empire building, and valuation

We conjecture that high stock liquidity negatively affects firm valuation by inducing inefficient investment. Using takeovers of public targets to study the empire-building motive, we find that a liquid firm is more likely than an illiquid firm to acquire a public firm. Such a takeover by a bidder with higher stock liquidity destroys bidder value to a larger degree. These patterns occur in both stock and cash acquisitions and hold after we use decimalization of tick size as a quasi-exogenous shock to stock liquidity. Finally, we show that financial constraints and corporate governance play important roles in the effects of stock liquidity on empire building in mergers and acquisitions.

On Bayesian Epistemology of Myerson Auction

Bayesian Epistemology bases its analysis of the objects under study on a prior, a (jointly known) probability distribution, which is in turn the subject matter in statistical learning, and that of machine learning at least implicitly. Our interests are in data related to bidder behavior in a game theoretical setting where the learner and the owners of data to be learned are affected by each other’s strategies of data reporting and data utilization. More specifically, the learner takes actions according to the probability governing the data distributions. The owners of data may reveal false probability distributions of their private data for their better utilities. We focus on the Myerson auction for such an issue of learning and optimization of the seller who wants to gain information on bidders’ value distributions to achieve the maximum revenue. We study bidders’ best responses against each other. We make a special effort to understand the possibility of truthful reporting of agent value distributions. For cases this is not possible, we study the dynamic properties of bidder value distribution reporting and the convergence toward a local Nash equilibria.

Assessment of green public procurement specifications in government institutions: Case study in Central Java, Indonesia

The construction industry has always been the biggest contributor to environmental damage. With the emergence of Leadership in Energy and Environmental Design (LEED) certification and Mayor Regulation Number 24 of 2019 concerning green buildings and Presidential Regulation in Indonesia concerning the importance of green industry products in the procurement process led to the emergence of urgency for the construction sector to create a focus on green. One focus that needs to be considered because contributing a large role in the construction process from beginning to end is the procurement process. The problem that occurs is that there is no awareness from Project Manager in Central Java is regarding environmental aspects in making procurement specifications. This is evident because the consideration of certification of Environmental Impact Assessment, and Occupational Health and Safety Regulation has not yet been taken into consideration in the specification of Project Manager considerations. As a result, government buildings have 46% lower energy efficiency compared to private buildings. With the DEMATEL method, it was found that the criteria “lowest bid value”, “Reducing the use of hazardous materials”, “Minimizing energy consumption”, “Minimizing energy loss”, “Clarifying environmentally friendly fuels”, “Impact on the environment during and after construction”, “Work procedures”; “Supervision”, and “Frequency of failure” is a criterion that is considered to influence other criteria is greater and can be an urgency for the government of Central Java.

Transparansi Pengadaan Barang dan Jasa Pemerintah Secara Elektronik (E-procurement) di Nusa Tenggara Barat

The purpose of this research is to identify and describe the transparency of the management of government procurement of goods / services electronically (e-procuremnet) in LPSE West Nusa Tenggara Province, and to find out how clear the information regarding the e-procurement process is presented by LPSE NTB. Using qualitative methods with a field study approach, literature studies and secondary and primary data processing from various existing sources. Data collection was also carried out by interviewing all parties involved and understanding the process of procuring government goods / services electronically (e-procurement). The research results show that procedurally the e-procurement process at LPSE NTB is good, because the public can access information related to the procurement of goods / services by the NTB Provincial Government on the LPSE NTB website. However, the information presented is not yet complete, there is information transparency regarding the auction process being carried out, such as 13 procurement packages that were not included Corrected Offer Value (Rp. 0) during 2017-2020 with a Budget Ceiling of Rp. 54,620,180,375. and there were 76 auction packages for the procurement of goods that failed so that a re-tender was carried out, but there was no clear information on why the auction could fail, there were also 76 auction packages that did not include the name of the winning company during 2017-2018 In the e-procurement system, one of the requirements that must be fulfilled is clarity of information regarding the entire process of procurement of goods / services from start to finish, clarity of information includes the type of procurement to be auctioned, budget ceiling value, value of HPS, corrected bid value, work unit conduct the procurement, the number of auction participants and the name of the winning company.

Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions

The connection between games and no-regret algorithms has been widely studied in the literature. A fundamental result is that when all players play no-regret strategies, this produces a sequence of actions whose time-average is a coarse-correlated equilibrium of the game. However, much less is known about equilibrium selection in the case that multiple equilibria exist. In this work, we study the convergence of no-regret bidding algorithms in auctions. Besides being of theoretical interest, bidding dynamics in auctions is an important question from a practical viewpoint as well. We study the repeated game between bidders in which a single item is sold at each time step and the bidder's value is drawn from an unknown distribution. We show that if the bidders use any mean-based learning rule then the bidders converge with high probability to the truthful pure Nash Equilibrium in a second price auction, in VCG auction in the multi-slot setting and to the Bayesian Nash equilibrium in a first price auction. We note mean-based algorithms cover a wide variety of known no-regret algorithms such as Exp3, UCB, \epsilon-Greedy etc. Also, we analyze the convergence of the individual iterates produced by such learning algorithms, as opposed to the time-average of the sequence. Our experiments corroborate our theoretical findings and also find a similar convergence when we use other strategies such as Deep Q-Learning.

Willingness To Pay Konsumen Pengguna Air Di Sub-Das Kali Dendeng – Kota Kupang

Global awareness towards climate change has been a serious concern for archipelago countries due to water shortage. This problem even causes more serious life-threating phenomenon because water is one of the main resources for energy circulation. Whether change anomalies have brought about impacts on seasonal shift on several islands of Indonesia, including those in East Nusa Tenggara province. These islands experience drought. Fresh water availability for industry and domestic use in cities like Kupang has been a serious problem. This issue becomes worse in recent years due to significant increase of city population. Public responsibility to maintain natural environment and resources is therefore required. This study tries to investigate public concerns over the water shortage issue in order (1) to know to what extent public is willing to pay their consumption of fresh water provided through Sub-DAS Kali Dendeng; and (2) to analyze factors that influence the willingness of the fresh water consumers to pay for environment services provided at Sub-DAS Kali Dendeng. Quantitative approach which relies on primary and secondary data gathered through interview and field observation is applied in this study. The data were analyzed by using Dichotomous Choice CVM, Turnbull Method, and Logistic Regression to find out WTP value. The results of this analysis are described within the environmental science perspective to find out the relationship among various factors for being water consumer responsibility towards water resources preservation at Sub-DAS Kali Dendeng in Kupang. The results of the analysis show that the willingness to pay for fresh water by consumer at Kelurahan Manutapen, Mantasi dan Airmata is significantly high. They are willing to pay between Rp. 3.362 to Rp.11.328 according to WTP calculation. This amount is much higher than that determined by the city freshwater supplier company (PDAM). The facts for the reason why the consumers are willing to pay for higher amount for fresh water supply are determined by several factors; they include bidding value, consumer’s level of education, consumer’s income, water quality, and the amount of the family members. These factors simultaneously influence the water consumer willingness to pay for the water they consume; and this willingness factor has significantly high impacts as seen in Negelkerke R. Square value at 0,600, or being comparable to 60%; meaning that various factors of 60% in Y are able to explain all free variable in X. Respondents for this study are those who use fresh water supplied from Sub-DAS Kali Dendeng, and those who reside around the Sub-DAS Kali Dendeng. It is believed that there must be more citizens around the Sub-DAS Kali Dendeng, who also contribute negative impact toward the ecological environmental change, yet these individuals have no direct responsibility, and therefore the responsibility for the environmental conservation examined trough this study might not be thoroughly measured by the amount of WTP they pay.

Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing

This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as “masters” which offload their computations to edge devices acting as “workers”. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master; ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.

Uma análise quantitativa do processo de concorrência em compras públicas dos pregões da Prefeitura Municipal de Pelotas

This work aimed to evaluate which factors determine the competition, measured by the winners’ number in each event, of the public bids held by the Municipality of Pelotas. The analysis was conducted taking the period from 2016 to 2017, observing the bids in the presential or electronic reverse auction format, aimed at the acquisition of common goods and services by the Public Administration at the lowest possible price. To achieve this goal, a database was built with information referring to 591 reverse auctions sessions available on the virtual page of the Rio Grande do Sul Province Court of Accounts and the Poisson regression model was applied. In summary, the results show that the monetary value of the public bid has a positive effect on the number of winners, with the increase of one unit of this variable increasing the number of winners on average by 12%. However, the impact of the contest has presented frustrated items reduces the number of winners on average by 30%. Additionally, it was found that the two-month increase in the time between the launch and the approval of the public bid on average has a positive impact of approximately 25% on the number of winners. These findings may suggest that purchases with a higher value and with a longer term may favor Public Administration in increasing competition and consequently in obtaining a lower unit price.

Willingness to Pay for Conservation of African Baobab Tree in Ethiopia (A Case Study of Abergele Woreda): Contingent Valuation Approach

ABSTRACT Conservation and restoration measures which consider the participation of the local community are necessary to save the baobab trees from being vanished. This study was conducted to assess communities’ average willingness to pay and identify factors that determines households’ willingness to pay (WTP) for the conservation and restoration of baobab tree. The study used primary data obtained from a survey conducted on 192 randomly selected households. Double bounded dichotomous choice (DBDC) elicitation format of contingent valuation method was employed. The data were analyzed using a bivariate probit model. The response of households’ for the scenario indicated that the mean WTP for the conservation and restoration of baobab tree was found to be 105 birr per year per household. The results of the model revealed that household’s age, annual income of household head, marital status of household head, distance and awareness of household have positive and significant effect on WTP. On the other hand, bid values of have a significant negative effect on WTP indicating that Households that are provided with lower initial bid value are more likely to be willing to contribute for conservation. Improving the livelihood of households, creation of awareness and setting fair contribution need to be considered to enhance community’s participation in baobab tree conservation.

Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption.

The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.

First price sealed-bid auction of the grey game improvement model via double-parameter Bayesian estimation

PurposeThe improved classical model makes it possible that the evaluation strategy has an optimal tendency, which reveals the purpose of this paper is to facilitate the first price sealed-bid auction more in line with the actual situation. To be more specific, there are several merits in the improvement process. On the one hand, the bid-winning probability can be improved for the bidder; on the other hand, the real market value of the subject matter can be more clearly recognized for the employer.Design/methodology/approachBayesian estimation and grey system theory are referenced in this paper, with the use of double-parameter estimation, little historical data and expert experience. Specific implementation steps are as follows: first of all, using the double-parameter Bayesian estimation to correct the actual valuation of the bid matter v, then introducing the threat factor grey number R in the auction model, giving the improving of the optimal grey quotation and grey expectation utility under the two-party game and finally taking the aerospace component procurement as an example, simulating the bidding process of the bidding parties to arrive at the optimal bid strategy.FindingsThe improved model shows that the optimal strategy will change with the threat factor rather than a fixed value. When the threat factor grey number R follows [0.4, 0.6], the optimal quotation strategy will appear, which means quotation is higher than 50% of the bid matter's valuation.Practical implicationsThe improved model proposed in this paper can strengthen the cost control in the Chinese commercial space process and optimize the pricing strategy for the final launch.Originality/valueThe modified model changes the habit that the bidder's valuation of the bid subject to mainly come from experience and to prompt the model for making full use of little historical data on the foundation of the former. It can reduce the subjective judgment error in the game results; finally, the practical cases are simulated in MATLAB at the same time, and the simulation effect is good, so we can get some more realistic conclusions on this basis.

Per-Hop Reversed Packet Auctions for Cooperative Routing in Mobile Wireless Networks

Many applications could benefit from multi-hop communications through users’ mobile devices. A key issue is how to incentivize users to cooperate in both routing and relay of messages by sharing their device’s precious resources. Previous works on the subject have either tackled cooperation in the relay of messages alone or in both routing and relay functionalities. In the latter case, path selection is usually carried out at the destination node, which renders significant delays because the selected path needs to be conveyed all the way back to the source node before any data packet can be transmitted. This is certainly unsuitable in mobile scenarios. This paper presents the performance of the “Tightness” strategy, which allows the routing and relaying of messages “on-the-go”, via per-hop reversed packet auctions. At each hop, the sender asks for bids from potential relays according to a “budget” attached to the data packet, through which the auction winner gets paid and can pay for others in subsequent auctions. The auction winner is chosen not only based on bid value, but also on the estimated relay’s likelihood to deliver the packet to destination. Likewise, each potential relay makes a bid considering its own chances to deliver the packet to destination. A fine is also announced in every auction, that must be paid by all relays if the packet is not delivered to destination within a “deadline” expressed in number of hops. The performance of the Tightness strategy is evaluated for both static and mobile scenarios and compared to two baseline strategies according to different performance metrics.

Revealed Wisdom of the Crowd: Informational Value of Bids

Traditional studies on the wisdom of the crowd focus on member characteristics that are either static (e.g., demographics) or slow to change (e.g., expertise). However, members of a crowd are different not only in terms of who they are but also what they do when faced with different opportunities. Using data from online debt crowdfunding, we propose and find significant informational value in the heterogeneity of observable actions of crowd members. We quantify investor behavior in two ways: bid size and bid timing. We find that loans funded with fewer but larger bids are less likely to default than those funded with a larger number of smaller bids. For loans with similar bid-amount distributions, those receiving larger bids earlier in the bidding process are less likely to default. These features also help improve the predictive performance of loan default models. Importantly, these features are easily accessible and less subject to privacy concerns. Our findings point to a novel direction of extracting wisdom of the crowd, new methods of evaluating loan quality in secondary markets, and have implications for the design of online crowdsourcing platforms in general.