Bid Values Research Articles

Empirical Review on Determinants of Willingness to Pay for Natural and Environmental Resource Valuation: Meta-Analysis

Even if natural and environmental resource plays detrimental role prosperity of nations, measuring their value is highly problematic. Lack of realistic markets data to estimate its value for the goods and services necessitates the use of nonmarket valuation techniques. The meta-analysis was carried out to see the mean size effect of certain demographic, socioeconomic and institutional factors on willingness to pay of natural resources conservation and improvement. The empirical analysis review was done on about 57 published articles for total of 116 observations. The data synthesis was done from published articles include coefficients of explanatory variable and standard error, model used, value measured, study region, sample size and publication from the year of 2002 up to 2019 year. The data analyzed in excel sheet and SPSS software. The study countries were Ethiopia, Uganda, Kenya, Guinea, Nigeria, Malaysia, USA, Iraq, China and India. The result confirmed that measurement errors and heterogeneity of case studies, interviewed resulted difference in willingness to pay estimate. The result implied inverse relationship between willingness estimate and environmental resource valued and study destination [Eat Africa]. The meta-analysis indicated mean size effect of willingness to pay defined as function of income, age, bid value, occupation, sex, knowledge, and education level of respondents. The empirical analysis result showed that increasing awareness, enabling to expand income earning believed to increase the willingness to pay value.

Smallholder farmers’ willingness to pay for improved irrigation water use: the case of Menz Mama Midir Woreda in North Shewa Zone of Amhara National Regional State, Ethiopia

ABSTRACT The water use practices in Menz Mama Midir woreda are very traditional and inefficient. To lessen this problem, water pricing (valuation) has been considered to be a promising tool. This study, therefore, examined smallholder farmers' willingness to pay and determinants affecting their decisions to pay for improved irrigation water use in Menz Mama woreda, North Shewa zone, Amhara national regional state of Ethiopia. Cross-sectional data collected from 215 randomly sampled irrigation beneficiaries were used for analysis. The mean WTP from double bounded dichotomous elicitation method ranges from 164.027 Birr (4.17 USD) to 221.059 Birr (5.62 USD) per year per hectare of irrigable land. The result from the seemingly unrelated bivariate probit model estimation revealed that the sex of the household head, total annual farm income, size of irrigable landholdings of the household, frequency of extension contacts, amount of credit, and dissatisfaction with the existing irrigation service positively and significantly influenced farmers' WTP decisions. In contrast, off-farm income and bid values were negatively related to WTP decisions. Therefore, the aforementioned factors affecting farmers' decisions to pay should be taken into consideration when constructing irrigation schemes in the study area and areas with similar economic and socio-cultural settings.

Highly expressed of BID indicates poor prognosis and mediates different tumor microenvironment characteristics in clear cell renal cell carcinoma

BackgroundStudies have found that BH3 interacting domain death agonist (BID) is closely related to the occurrence and development of many kinds of tumors. However, little attention has been paid to the situation of BID in clear cell renal cell carcinoma (ccRCC). So, our aim was to explore the effect of BID in ccRCC.MethodsSurvival analysis, ROC curve, correlation analysis and Cox regression analysis were executed to analyze the prognostic value and clinical correlation of BID in ccRCC. The risk prognosis model was constructed in the training cohort and further validated in the internal testing cohort, ICGC cohort, and GEO cohort. Transcriptome sequencing and immunohistochemical staining of clinical specimens were used to validate the results of bioinformatics analysis. The GSEA, ESTIMATE algorithm, CIBERSORT algorithm, ssGSEA, TIDE score, correlation and difference analysis were used to analyze the effects of BID on immune infiltration in tumor microenvironment (TME).ResultsBID was highly expressed in ccRCC tissues, which was verified by transcriptome sequencing and immunohistochemical staining of clinical specimens. Patients with high expression of BID had a worse prognosis. BID is an independent prognostic factor for ccRCC. The prognostic model based on BID can accurately predict the prognosis of patients in different cohorts. In addition, the expression levels of BID was closely related to immunomodulatory molecules such as PD-1, LAG3, and CTLA4. Enrichment analysis indicated that BID was significantly enriched in immune-related responses and cancer-related pathways. The change of BID expression mediates different characteristics of immune infiltration in TME.ConclusionsBID is highly expressed in ccRCC, which is a reliable biomarker of ccRCC prognosis. It is closely related to TME, and may be a potential target for immunotherapy in patients with ccRCC.

Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price Landscapes

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions.

Rural Households' Demand Status for Mitigation of Prosopis juliflora (Sw.) DC Invasion and Its Determinant Factors in Ethiopia: Empirical Evidence from Afar National Regional State.

Ethiopia is among the world's poorest nations, and its economy is growing extremely slowly; thus, the government's budget to manage environmental amenities is not always sufficient. Thus, for the provision of environmental management services such as the eradication of Prosopis juliflora, the participation of local households and other stakeholders is crucial. This study is therefore initiated with the objective of assessing rural households' demands for mitigating Prosopis juliflora invasion in the Afar Region of Ethiopia. A multistage sampling technique was employed to obtain the 313 sample rural households that were used in the analysis, and those sample households were selected randomly and independently from the Amibara and Awash Fentale districts of Afar National Regional State, Ethiopia. In doing this, a seemingly unrelated bivariate probit model was used to determine factors affecting rural households' demands for mitigating Prosopis juliflora invasion. Consequently, as per the inferential statistical results, there was a significant mean/percentage difference between willing and nonwilling households for the hypothesized variables, except for some variables such as farm experience; years lived in the area, distance from the market, and dependency ratio. Furthermore, the seemingly unrelated bivariate probit model result indicates that sex, family size, tenure security, livestock holding, frequency of extension contact, and years lived in the area were important factors influencing the willingness to participate in Prosopis juliflora management practices positively, whereas age, off-farm/nonincome, and bid value affected willingness to pay negatively and significantly. Hence, to improve the participation level of households, policymakers should target these variables.

On joint distribution of bids in some k-th price auction model

In the era of increasingly widespread use of electronic communication tools, constant growth of e-commerce turnover is hardly surprising. One of its mechanisms are online auctions whose turnover, on a global scale, is enormous. This paper proposes a stochastic model of the value of subsequent actual bids appearing in the course of a dynamic internet k-th price auction. On this basis, it was possible to determine unconditional joint probability distributions, as well as univariate marginal distributions of successive bids appearing in the course of the auction type under consideration.

Maximizing the Success Probability of Policy Allocations in Online Systems

The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we consider the problem at the level of user timelines instead of individual bid requests, manipulating full policies (i.e. pre-defined bidding strategies) and not bid values. In order to optimally allocate policies to users, typical multiple treatments allocation methods solve knapsack-like problems which aim at maximizing an expected value under constraints. In the specific context of online advertising, we argue that optimizing for the probability of success is a more suited objective than expected value maximization, and we introduce the SuccessProbaMax algorithm that aims at finding the policy allocation which is the most likely to outperform a fixed reference policy. Finally, we conduct comprehensive experiments both on synthetic and real-world data to evaluate its performance. The results demonstrate that our proposed algorithm outperforms conventional expected-value maximization algorithms in terms of success rate.

Learning Coalition Structures with Games

Coalitions naturally exist in many real-world systems involving multiple decision makers such as ridesharing, security, and online ad auctions, but the coalition structure among the agents is often unknown. We propose and study an important yet previously overseen problem -- Coalition Structure Learning (CSL), where we aim to carefully design a series of games for the agents and infer the underlying coalition structure by observing their interactions in those games. We establish a lower bound on the sample complexity -- defined as the number of games needed to learn the structure -- of any algorithms for CSL and propose the Iterative Grouping (IG) algorithm for designing normal-form games to achieve the lower bound. We show that IG can be extended to other succinct games such as congestion games and graphical games. Moreover, we solve CSL in a more restrictive and practical setting: auctions. We show a variant of IG to solve CSL in the auction setting even if we cannot design the bidder valuations. Finally, we conduct experiments to evaluate IG in the auction setting and the results align with our theoretical analysis.

Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks

Powered by data-driven technologies, precision agriculture offers immense productivity and sustainability benefits. However, fragmentation across farmlands necessitates distributed transparent automation. We developed an edge computing framework complemented by auction mechanisms and fuzzy optimizers that connect various supply chain stages. Specifically, edge computing offers powerful capabilities that enable real-time monitoring and data-driven decision-making in smart agriculture. We propose an edge computing framework tailored to agricultural needs to ensure sustainability through a renewable solar energy supply. Although the edge computing framework manages real-time crop monitoring and data collection, market-based mechanisms, such as auctions and fuzzy optimization models, support decision-making for smooth agricultural supply chain operations. We formulated invisible auction mechanisms that hide actual bid values and regulate information flows, combined with machine learning techniques for robust predictive analytics. While rule-based fuzzy systems encode domain expertise in agricultural decision-making, adaptable training algorithms help optimize model parameters from the data. A two-phase hybrid learning approach is formulated. Fuzzy optimization models were formulated using domain expertise for three key supply chain decision problems. Auction markets discover optimal crop demand–supply balancing and pricing signals. Fuzzy systems incorporate domain knowledge into interpretable crop-advisory models. An integrated evaluation of 50 farms over five crop cycles demonstrated the high performance of the proposed edge computing-oriented auction-based fuzzy neural network model compared with benchmarks.

EWA‐DA‐based resource allocation method for 5G networks

AbstractIn 5G networks, most D2D work focuses on optimizing system throughput without addressing economic constraints. This article encourages users to exchange resources and apply honest bidding tactics to attain high auction efficiency. This study describes an experience‐weighted attraction double auction (EWA‐DA) technique for allocating resources in 5G D2D networks. The suggested method classifies mobile users into seller cellular users (SCU) and buyer cellular users' (BCU). The SCU transmits the beacon packet, and the nearby cellular users create a D2D group in response to the beacon considered BCU. Then, the bid values of the buyer and seller cellular users are transmitted to the base station side. The BS uses an experience‐weighted attraction‐learning model to obtain accurate bidding prices. Lastly, an ask‐to‐mean‐value double auction model was utilized instead of accurate bid values. Extensive simulation results demonstrate that the suggested strategy delivers excellent auction efficiency and fair earnings for both seller and buyer users. In addition, we identified that the participating users accomplished truthful bidding and used fewer resource blocks than existing techniques.

Incentive Auction Design Alternatives: A Simulation Study

This paper revisits the descending clock “reverse” auction design used in the U.S. Federal Communications Commission’s 2016–2017 “incentive auction.” We use extensive computational simulations to investigate the quantitative significance of various aspects of the design, leveraging a reverse auction simulator and realistic models of bidder values. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: This work was supported by the Defense Advanced Research Projects Agency [Grant FA8750-19-2-0222 CFDA\# 12.910], the Canadian Institute for Advanced Research [Canada AI Research Chair at the Alberta Machine In], the National Science Foundation [Grant 1525730], and the Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada [Discovery Grant, Discovery Grant Supplement]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2020.02489 .

Farmers’ Willingness to Pay for Climate Smart Agriculture in Flood Vulnerable Areas of Assam

The current study was carried out during 2020 in flood vulnerable district of Assam to measure the farmers’ Willingness to Pay (WTP) for climate smart agriculture practices and factors likely to influence thereon. The study reveals that degree of farmers ‘willingness to pay was found high for ‘Stress Tolerance Varieties of rice (STVs)’, ‘Integrated Nutrient Management (INM)’ and ‘Integrated Pest Management (IPM)’ while low degree of WTP was found for ‘Vermicompost preparation and application (VC)’ and ‘Minimum Tillage (MT)’. The highest average bidding value was recorded for STV (Rs. 251.00) followed by INM (Rs. 185.00) and IPM (Rs. 174.13) while for VC and MT the average bidding value was Rs. 158.63 & Rs 79.75 respectively. The LMR analysis confirmed that the selected explanatory variables expressed variation in willingness to pay for CSA practices about 74% (R2=0.74). The variable ‘educational status’ (x2), ‘accessible to market’ (x7) ‘cropping intensity’ (x9), ‘adoption consistency’ (x13) and ‘preferences to CSA practices’ (x14) were found to have positive while variable ‘age’ (x1) and ‘institutional contact’ (x6) have negative and significant influence on WTP. Popularizing CSA practices available in the research front, various organizations (both public and private) in input and output chains should work together as a common entity so that farmers get necessary nurture for adoption of technologies.

Establishing cost-effectiveness threshold in China: a community survey of willingness to pay for a healthy life year

IntroductionThe willingness to pay per quality-adjusted life year gained (WTP/Q) is commonly used to determine whether an intervention is cost-effective in health technology assessment. This study aimed to evaluate the...

HOUSEHOLDS’ WILLINGNESS TO PAY FOR NATURAL HAZARD INSURANCE IN THE SOUTHWESTERN COASTAL REGION OF BANGLADESH

The southwestern coastal region of Bangladesh is enormously susceptible to natural hazards due to global environmental change, triggering economic instability in coastal households every year. Hazard microinsurance might be a decent option to build financial resilience; considering the assorted consumers’ preferences is essential for policy scheming. Therefore, this research estimated households’ willingness to pay (WTP) for natural hazard insurance (NHI) in Bagerhat Sadar and Rampal upazila of Bagerhat district in southwestern coastal Bangladesh. The contingent valuation method’s double-bounded dichotomous choice (DBDC) technique was exerted on the 120 randomly interviewed households’ dataset to estimate the WTP for NHI considering three experimental bid values (i.e., initial bid of BDT 300, maximum bid of BDT 500, and lower bid of BDT 200 as the NHI premium per month). This assessment revealed that, on average, around three-fourth of coastal household experienced financial losses (i.e., damage to crops, fish-shrimp enclosures, integrated farms, and houses) yearly due to natural hazards. Almost two-third of the total sample was interested in taking such alternative financial support after a massive coastal hazard, and the mean WTP was approximately BDT 284. Amongst those households, more than four-fifth were willing to pay for the initial bid value. Moreover, a logit analysis was performed to identify WTP’s probability and maximum likelihood; the Pearson goodness-of-fit test to estimate the chi-square ( =5.29; p<0.01) value; and Kendall’s tau-b ( ) test to clarify the level of significance and associations [Highest correlation-coefficient (WTP and initial bid) =0.837**; **p<0.01; strongly associated; optimal value] between WTP and three experimental bid values. Besides, this research has attempted to give policymakers a vivid understanding of households’ behavior in adopting such microinsurance. Government and development agencies are highly recommended to implement such a microinsurance scheme as post-hazard economic facilitation for coastal victims.

A Study on the Estimation of the Value of Tourists’ WTP by Payment Method: Focusing on the Gangwon State

Purpose - This study estimated the value of tourists’ willingness to pay (WTP) to develop and promote regional tourism and minimize external effects using the contingent valuation method (CVM).
 Design/methodology/approach - To reduce exaggeration bias, a major issue of CVM, a pilot survey was conducted to categorize bid value. Additionally, the payment method was divided into a tourism fund and tourism taxes, and the respondents’ answers were reaffirmed in two stages.
 Findings - As a result of the analysis, the tourists’ WTP was estimated to be 5,132 KRW (3.60 USD) for the tourism fund and 1,036 KRW (0.73 USD) for tourism taxes.
 Research implications or Originality - The results of this study provide implications for securing financial resources and establishing policies in Gangwon province, which promotes special self-governing.

Financial Analysis of Solar PV Tariff Bids

A Discounted Cash Flow - based analysis of the Levelized Cost of Energy for the lowest Solar PV bid of INR 2.44 / kWh, and a model that estimates the same to within 10% of bid value - obtaining a tariff of INR 2.67 /kWh. Followed by a Deterministic Sensitivity Analysis of the key factors affecting LCOE, and a qualitative analysis of the policy factors, financial mix decisions and technological improvements that shape the variation in the lowest Solar PV tariff bids, from 2010 Launch of the JNNSM to 2017, when the analysed bid was recorded.

Deep Reinforcement Learning Based Real-Time Renewable Energy Bidding With Battery Control

Recently, various renewable energy sources and large-scale batteries have been integrated into power grids, and renewable energy bidding and battery control become critical problems in the real-time energy market. However, bidding and control problems have been studied separately while these two problems simultaneously influence the total profit of renewable producers. In this paper, we propose a novel strategy where renewable energy bidding and battery control are collectively investigated. First, unlike the previous studies where bidding is simply the forecasted value, the proposed methods determine the bidding values considering the error compensability of the battery by switching the objective of forecasting from reducing errors to making errors compensable. After the error compensation, additional battery control is applied to utilize the energy arbitrage process considering the energy price. As there are energy price and renewable generation uncertainties, we propose a deep reinforcement learning based bidding combined with control, called DeepBid, for sequential decision making under uncertainty. Our extensive simulations with real solar and wind generation data show that the proposed DeepBid strategy substantially increases the total profit compared to existing bidding strategies by achieving as high revenues as the arbitrage strategy and as low deviation penalties as the error compensation strategy.

Routing battery-constrained delivery drones in a depot network: A business model and its optimization–simulation assessment

This paper proposes a novel business model for on-demand package shipment services using drones, and evaluates different modeling and solution approaches for the drone routing problem that underpins the service operation. In the proposed service, customers’ shipment orders of arbitrary origins and destinations, payload weights and bid values are collected every five minutes, and available drones from multiple depots are then dispatched to fulfill a subset of these orders in a way to maximize profit. A drone path starts from a depot, serves one or more customer orders in sequence, and ends at a depot for battery recharging, which incurs a fixed cost. Two mixed integer programming (MIP) formulations are presented to model the drone dispatch and routing problem. To improve solution efficiency, three computational approaches, including two column generation based algorithms and a brute-force path enumeration algorithm, are developed and compared. Computational experiments suggest, somewhat surprisingly, that the brute-force approach is the most effective and most scalable one, outperforming other alternatives by a substantial margin in both computing time and solution quality. Furthermore, an optimization–simulation framework is proposed to assess the system performance over a long horizon that spans multiple dispatch periods without complicating the optimization model. Using the simulation framework, useful managerial insights including the effects of battery capacity, wind condition and computing capability on the fleet dispatch operation, are generated, which will guide real-world implementations of the new business model.

157 Thoroughbred and auction characteristics that impact bid price when sold at online auctions

Thoroughbreds are best known for their careers as racehorses, a career that often concludes between the ages of 3 and 6. After racing, Thoroughbreds can perform successfully in other disciplines like eventing and dressage. However, the owner demand for off-the-track Thoroughbreds (OTTBs) as mounts for second careers is not well characterized. Therefore, the objective of this study is to quantify the buyer demand for OTTBs that have started second riding careers and are being marketed through a virtual auction. Horse and auction characteristics were collected on 170 Thoroughbred horses across 39 online sport horse auctions between 2012 and 2020. Data were analyzed using ANOVA (α = 0.05) and mean separations were assessed using Tukey's HSD when appropriate (STATA 16). Bids ranged from $100 to $20,000 and the average was $2,528.25 ± 3,011.37 (mean ± SD). Horse age impacted (P = 0.001) the bid price, where each additional year increased bid price by $430.50 ± 158.7. However, when the age was squared to model an aging horse, bid price decreased (P = 0.001) with age (-$22.91 ± 6.74). Additionally, geldings brought a premium of $924.08 ± 427.84 over mares, displaying buyer preference for geldings. Horse color, especially chestnuts ($1,071.07 ± 555.28;P = 0.05) and grays or roans ($1,054.05 ± 536.41; P = 0.05), brought premiums over bay or black horses. Horse height did not (P > 0.05) impact bid price, nor did Jockey Club registration (P > 0.05) or previous training in most English riding disciplines (P > 0.05). Horses listed as trail riding mounts, though, received discounts (-$1,599.46 ± 604.58; P = 0.001) compared with other disciplines. Of the auction logistics, only registration in USEF or USHJA or USEA affected (P = 0.01) bid price, where registration increased sale price by $1,967.33 ± 820.24. This interest shows that bidders value horse registration and could suggest a valuable tool for sellers to increase their horse's bid price. By characterizing the discounts and premiums for horse and auction characteristics, equine bidders can better expect what a horse may sell for, and which attributes could lend them more competition when actively bidding. Additionally, sellers can use this information to not only calculate what premiums or discounts their horse may sell with, but sellers can increase their horse's bid by enhancing their profile with registration.

A comparison of reward processing during Becker-DeGroot-Marschak and Vickrey auctions: An ERP study.

Vickrey auctions (VA) and Becker-DeGroot-Marschak auctions (BDM) are strategically equivalent demand-revealing mechanisms, differentiated only by a human opponent in the VA, and a random-number-generator opponent in the BDM. Game parameters are such that players are incentivized to reveal their private subjective values (SV) and behavior should be identical in both tasks. However, this has been repeatedly shown not to be the case. In this study, the neural correlates of outcome feedback processing during VA and BDM were directly compared using electroencephalography. Twenty-eight healthy participants bid for household products which were then divided into high- and low-SV categories. The VA included a human opponent deception to induce a social environment, while in reality a random-number-generator was used in both tasks. A P3 component peaking at 336 ms over midline parietal sites showed more positive amplitudes for high bid values, and for win outcomes in the VA but not the BDM. Both auctions also elicited a Reward Positivity potential, maximal at 275 ms along the central midline electrodes, that was not modulated by auction task or SV. Further, an exploratory N170 potential in the right occipitotemporal electrodes and a vertex positive potential component were stronger in the VA relative to the BDM. Results point to an enhanced cortical response to bid outcomes during VA task in a potential component associated with emotional control, and to the occurrence of face-sensitive potentials in VA but not in BDM auction. These findings suggest modulation of bid outcome processing by the social-competitive aspect of auction tasks. Directly comparing two prominent auction paradigms affords the opportunity to isolate the impact of social environment on competitive, risky decision-making. Findings suggest that feedback processing as early as 176 ms is facilitated by the presence of a human competitor, and later processing is modulated by social context and subjective value.