We study binary decision-making in governance councils of Decentralized Autonomous Organizations (DAOs), where experts choose between two alternatives on behalf of the organization. We introduce an information structure model for such councils and formalize desired properties in blockchain governance. We propose a mechanism assuming an evaluation tool that ex-post returns a boolean indicating success or failure, implementable via smart contracts. Experts hold two types of private information: idiosyncratic preferences over alternatives and subjective beliefs about which is more likely to benefit the organization. The designer's objective is to select the best alternative by aggregating expert beliefs, framed as a classification problem. The mechanism collects preferences and computes monetary transfers accordingly, then applies additional transfers contingent on the boolean outcome. For aligned experts, the mechanism is dominant strategy incentive compatible. For unaligned experts, we prove a Safe Deviation property: no expert can profitably deviate toward an alternative they believe is less likely to succeed. Our main result decomposes the sum of reports into idiosyncratic noise and a linearly pooled belief signal whose sign matches the designer's optimal decision. The pooling weights arise endogenously from equilibrium strategies, and correct classification is achieved whenever the per-expert budget exceeds a threshold that decreases as experts' beliefs converge.
Intherapidlyevolvingdigitallandscape,freelancing platforms face significant challenges due to a lack of transparency,trust,andcentralizedcontrol.Thispaperpresents the design and implementation of a blockchain-powered web- based project management system integrated with a visual data dashboard. The proposed system leverages Ethereum smart contractstoensuresecure,tamper-proofuserregistration,project posting, bidding,assignment, work submission, payment release, and rating. The backend is developed using Django, while blockchain integration is achieved via Web3.py, enabling secure and transparent interactions. The platform provides real-time analyticsonusers,jobstatus,fundmovement,andratingsthrough a dashboard. The solution enhances trust, transparency,and de- centralization,provingeffectiveforfreelanceprojectecosystems
Vahab Esfandani, Mohammad Amin Borghei, Sara Ravan Ramzani, Peter Konhaeusner · 6 authors
The digital economy has expanded organizations’ ability to source ideas, labor and capital through online participation, making crowdsourcing a strategic mechanism for innovation and problem solving. This chapter conceptualizes strategic crowdsourcing as a socio-technical system rather than ad hoc task outsourcing and synthesizes dispersed theory and evidence into a coherent framework for design and governance. It defines major typologies—micro-tasks, open innovation contests, co-creation, crowdfunding, internal crowdsourcing and citizen science—and situates them relative to outsourcing and open-source collaboration to clarify when each approach fits task uncertainty, required expertise and desired ownership of outputs. Building on open innovation, socio-technical systems and participatory governance perspectives, the chapter proposes an integrated model with five linked layers: contextual drivers; input configuration (task specification, crowd definition and call design); enabling infrastructure (platforms and technologies, including AI and blockchain-based mechanisms); process mechanisms (incentive design, validation and quality assurance, data governance and ethical/legal safeguards); and outputs/outcomes (innovation, organizational learning, governance effects and social value with feedback loops). Cross-sector illustrations from technology, healthcare, education, civic tech and sustainability highlight recurring trade-offs around motivation, quality control, fair compensation, privacy and confidentiality and intellectual property rights. The chapter also evaluates emerging hybrid human–AI crowdsourcing and decentralized autonomous organizations (DAOs), emphasizing that their benefits depend on transparent rules, accountable allocation of rewards and decision rights and human-in-the-loop oversight to mitigate bias, concentration of control and trust failures. Overall, strategic crowdsourcing is positioned as potentially democratizing when aligned with organizational goals and governed responsibly. It concludes by outlining research directions for comparative studies, cross-cultural analysis and regulation-aware design.
Industrial carbon emissions play a major role in environmental pollution and climate change. Because of this, industries are required to continuously monitor their emissions and ensure they follow environmental regulations. Traditional emission monitoring systems generally rely on centralized databases, which can sometimes lead to problems such as delayed reporting, lack of transparency, and the possibility of data being altered. To overcome these issues, this paper introduces CarbonChain, a decentralized carbon emission monitoring system that combines Internet of Things (IoT) sensing technologies with blockchain verification. Environmental parameters such as gas concentration and particulate matter are collected in real time using sensors connected to microcontroller units. The sensor readings are then transmitted to a backend server where the data is validated and categorized. After validation, the emission records are stored on the blockchain through smart contracts, generating secure transaction hashes that ensure the integrity of the data. A web-based dashboard allows regulators and industry stakeholders to monitor emission levels, check compliance status, and verify blockchain records in real time. By combining IoT-based sensing with blockchain technology, CarbonChain creates a transparent and tamper-resistant monitoring platform that can support environmental auditing and carbon credit verification.
Day after day, climate change intensifies, necessitating tracking solutions for carbon emissions that offer transparent operations and efficiency, alongside scalability and sustainable behavioural incentives. The proposition to track carbon emissions is not new, yet standard tracking systems present multiple deficiencies, including double reporting, fraud, high operational costs, and constrained access for small organisations. We have developed a blockchain system that follows a framework to track both carbon emissions and trading activities, using smart contracts and decentralised ledger technologies to establish security, trust, and automation. Our system requires IoT sensor integration and AI analytics to enable continuous monitoring and safe storage, along with direct carbon trading without third-party involvement. The proposed framework addresses blockchain energy consumption issues by examining Proof of Stake (PoS) and hybrid consensus models. The model presented facilitates a massive reduction in carbon emissions and enhances transparency and efficiency.
Effective traceability management in mineral resource development faces persistent challenges including information asymmetry, data falsification, and verification difficulties across complex value chains. This paper proposes a comprehensive blockchain-enabled traceability evaluation framework integrating distributed ledger technology with systematic assessment methodologies. A four-layer architecture encompassing data acquisition, blockchain storage, analysis processing, and evaluation application is designed to ensure data integrity throughout the mineral lifecycle. A hierarchical indicator system spanning five dimensions-traceability breadth, depth, precision, timeliness, and data credibility-is constructed, with the Analytic Hierarchy Process employed for weight determination and fuzzy comprehensive evaluation applied for performance assessment. Empirical validation through case study analysis of Huaxin Mining Group demonstrates the framework's practical applicability, yielding a comprehensive traceability score of 81.2 (Good grade). Comparative analysis reveals that blockchain-based systems achieve 96.8% data accuracy versus 82.4% for traditional approaches, with trace-back efficiency improving from 127.3 min to 4.7 min. The blockchain technology contribution ratio reaches 47.3% toward maximum traceability improvement. These findings provide theoretical foundations and practical guidance for advancing transparent and accountable mineral resource governance.
D. Hema Lakshmi, B. Prem Sai Siddhik, B. Akhil Kumar, Ch. Siva Venkata Sai Tharun · 5 authors
Resumes are a key part of traditional hiring, but when human reviewers may not accurately identify the true skills of candidates. Sometimes, when checks are done, fraudulent credentials may pass undetected due to limitations in manual verification. A new method is presented here that uses smart algorithms in a distributed ledger system. By connecting machine learning with secure data records, trust in verifying applicants grows a lot. The proposed system improves efficiency by reducing reliance on traditional keyword-based filtering. The software uses natural language tools to look at the applicant's information, extracts relevant skills and generates a performance score for each candidate. Cryptographic hashes of credentials are stored on a distributed ledger, ensuring that validation cannot be altered or hacked. An online model was created using ReactJS, Flask, MongoDB, and connections to the Ethereum Blockchain. The results show that the method automatically sorts job applicants, quickly checks their documents, and consistently finds qualified people in different fields. Combining smart algorithms with decentralised records increases trust, cuts down on manual tasks, and brings more clarity to the hiring process.
This study evaluates the robustness of Proof of Team Sprint (PoTS) against adversarial attacks through simulations, focusing on both the attacker win rate and computational efficiency under varying team sizes (N) and attacker ratios (α). PoTS is a recently proposed consensus mechanism that relies on randomly formed teams of participants to collaboratively generate blocks. Unlike traditional consensus methods where individual nodes compete independently, PoTS distributes responsibility across multiple nodes in a team, thereby increasing resilience against coordinated attacks. Our simulation results demonstrate that PoTS effectively reduces an attacker’s ability to dominate the consensus process, even under challenging conditions. For instance, when α ＝ 0.5, the attacker win rate decreases from 50.7% at N ＝ 1 to below 0.4% at N ＝ 8, effectively neutralizing adversarial influence. Similarly, at α ＝ 0.8, the attacker win rate drops from 80.47% at N ＝ 1 to only 2.79% at N ＝ 16, highlighting PoTS’s robustness under extreme threat levels. In addition to its strong security properties, PoTS maintains high computational efficiency by synchronizing block generation within each team. We introduce the concept of Normalized Computation Efficiency (NCE) to quantify this efficiency gain, demonstrating that PoTS significantly improves resource utilization as team size increases. As N grows, PoTS not only enhances security but also achieves better computational efficiency due to the averaging effects of execution time variations among team members. These findings underscore PoTS as a promising and practical alternative to traditional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS). By leveraging team-based block generation, sequential execution, and randomized participant reassignment in each round, PoTS provides a scalable, resilient, and energy-efficient framework for decentralized consensus in blockchain systems.
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most focus primarily on algorithmic design and overlook system-level challenges, including coordination efficiency, secure handling of model updates, and practical usability. We present FWeb3, a practical Web3-enabled FL framework for incentive-aware training in open environments. FWeb3 adopts a modular architecture that separates FL functions from Web3 support services, decoupling the off-chain training and data plane from on-chain settlement while preserving verifiable incentive execution. The framework supports pluggable aggregation and contribution evaluation methods and provides a browser-native DApp interface to lower the participation barrier. We evaluate FWeb3 in real-world settings and show that it supports end-to-end incentive-aware FL with transaction and data-transfer overheads of only 21.3% and 3.4% in WAN; FWeb3 also deploys from zero configuration in under 3 minutes and enables user onboarding in under 1 minute.
The integration of distributed data storage, P2P networks, consensus mechanisms, cryptography and other technologies, the application of blockchain technology has expanded from the initial financial field to many other areas, such as logistics and auditing. The consensus mechanism is the soul of blockchain technology, and it is of great significance to conduct a rigorous mathematical analysis. As far as we know, the Proof of Stake (PoS) consensus mechanism is only a qualitative description of the rich and the poor, the rich are richer, the poor are poorer, and there is no quantitative mathematical analysis. This paper presents a novel quantitative framework to quantitatively analyze the PoS consensus mechanism. Under the premise of not carrying out the attack, we use the expected reward and the reward ratio as the evaluation indicators, quantitatively analyze the optimal fund allocation strategy of the two parties game under the PoS consensus mechanism from the perspective of rich miners, and construct the reward function as the objective function. The inequality constrains the optimization problem and solves it using the Karush-Kuhn-Tucker condition. We consider the two schemes of assignment strategy and random strategy, and get the optimal fund allocation strategy. At the same time, it is compared with the general strategy to obtain the optimization effect of the optimal strategy. After that, we compare the situation in which both sides of the game use the optimal strategy. We found that for assignment strategy, the mining activity will not indicate that the rich are richer and the poor are poorer. However, for the random strategy, this will not happen. The random strategy is also the most common strategy in practice. We also use Markov decision process (MDP) to give the optimal strategy calculation method under the rational miner game, which is also applicable to the n-parties game. The work of this paper helps the blockchain developers to analyze the PoS consensus mechanism, and the adoption strategy of the assignment strategy and the random strategy can be used as the future research direction.
Abstract. The Bitcoin blockchain is a distributed ledger of transactions maintained by a network of nodes. The protocol assumes that honest nodes control a majority of the network's computing power. Conventional wisdom suggests that a minority group cannot earn revenue disproportionate to its hashing power, implying that the rational strategy is to remain honest. However, Eyal and Sirer (2013) challenged this view by introducing "Selfish Mining," a strategy that enables a minority pool to earn rewards exceeding its share of computing power. This paper replicates the original study using Monte Carlo simulations to verify the threshold at which this attack becomes profitable. The results confirm that a pool controlling more than 1/3 of the network hashrate can theoretically achieve higher returns than honest mining.
In blockchain systems operating under the Proof-of-Stake (PoS) consensus mechanism, fairness in transaction processing is essential to preserving decentralization and maintaining user trust. However, with the emergence of Maximal Extractable Value (MEV), concerns about economic centralization and content manipulation have intensified. To address these vulnerabilities, the Ethereum community has introduced Proposer Builder Separation (PBS), which separates block construction from block proposal. Later, enshrined Proposer Builder Separation (ePBS) was also proposed in EIP-7732, which embeds PBS directly into the Ethereum consensus layer. Our work identifies key limitations of ePBS by developing a formal framework that combines mathematical analysis and agent-based simulations to evaluate its auction-based block-building mechanism, with particular emphasis on MEV dynamics. Our results reveal that, although ePBS redistributes responsibilities between builders and proposers, it significantly amplifies profit and content centralization: the Gini coefficient for profits rises from 0.1749 under standard PoS without ePBS to 0.8358 under ePBS. This sharp increase indicates that a small number of efficient builders capture most value via MEV-driven auctions. Moreover, 95.4% of the block value is rewarded to proposers in ePBS, revealing a strong economic bias despite their limited role in block assembly. These findings highlight that ePBS exacerbates incentives for builders to adopt aggressive MEV strategies, suggesting the need for future research into mechanism designs that better balance decentralization, fairness, and MEV mitigation.
ABSTRACT Over the years, numerous efforts have been undertaken to accurately forecast traffic conditions and thereby preventing additional congestion. However, existing crowd management techniques focus on recognizing and counting the crowd while leaving the security of crowd information. A typical crowd management system is centralized and faces challenges, such as contributor selection reliability, fair payment evaluation, privacy concerns and high deployment costs. This study investigates security concerns in crowd management and evaluates the potential of blockchain technology to improve crowd management security. Combining the power of blockchain (decentralization and security) and smart contracts, this work proposes a secure crowd management architecture named . The framework operates on blockchain, utilizes cryptographic algorithms, and incorporates reputation management along with credit distribution through smart contracts. effectively safeguards crowd data while its revenue structure entices users to actively contribute to the system. has been simulated on GoQuorum's Ethereum private blockchain, using elliptic curve signatures for secure and efficient processing. Its performance was tested with RAFT, PoA and IBFT consensus mechanisms where RAFT led in throughput, IBFT lagged and PoA offered a middle ground. PoA stands out for balancing scalability and security, supporting network growth while preserving identity‐based validation and data integrity.
This thesis advances privacy-preserving solutions essential for addressing contemporary technological challenges in smart cities, decentralized systems, and algorithmic decision-making processes. Firstly, we introduce a hybrid sensing framework integrating Internet of Things (IoT) sensors and crowdsensing techniques to overcome limitations inherent in traditional methods. The hybrid sensing model incentivizes voluntary user contributions to complement fixed-location IoT sensors, ensuring reliable and comprehensive data collection while maintaining user anonymity through a privacy-preserving protocol. We implement this model in a smart parking application, demonstrating significant improvements in data accuracy and user engagement. Secondly, we propose a decentralized anonymous crowdsourcing system leveraging blockchain technology, which removes reliance on centralized intermediaries, thereby enhancing transparency and mitigating biases. Our system integrates anonymous payments using the Zerocoin protocol framework, eliminating the need for worker identity registration and trusted setups, thus fostering genuinely anonymous participation. Empirical analyses confirm that our approach maintains practical efficiency in transaction verification and moderate blockchain gas costs. Lastly, we tackle fairness and transparency in algorithmic decision-making processes, addressing public concerns regarding inherent biases and opaque computational practices. We develop a privacy-preserving, publicly verifiable framework that combines succinct zero-knowledge proofs with blockchain infrastructure, allowing independent verification of algorithmic fairness without exposing sensitive inputs or decision-making algorithms. Our concrete instantiation employs a restricted KZG polynomial commitment scheme alongside the Sonic zk-SNARK protocol, demonstrating small proof sizes, efficient verification, and practical deployment feasibility. Collectively, this thesis contributes significantly to the field by providing robust, scalable, and privacy-conscious technologies tailored for contemporary smart city applications and decentralized computational ecosystems.
Ensuring efficient task assignment and secure payment in mobile crowdsensing while preserving worker location privacy remains a challenging problem. Existing solutions either rely on expensive encryption schemes, employ blockchain-based verification that incurs high computational and gas costs, or use differential privacy techniques that degrade spatial accuracy. This paper introduces the Privacy-preserving Task Assignment and Payment (PTAP) framework, a lightweight solution built upon secure multi-party computation (SMPC). PTAP employs additive secret sharing and a challenge-response mechanism across three semi-honest servers to achieve anonymous task allocation and payment without blockchain or zero-knowledge proofs. The framework guarantees full unlinkability between worker identities, task locations, and payment records while maintaining accurate location-based assignment and supporting traceability for dispute resolution. Experimental evaluation using the MP-SPDZ framework demonstrates scalability to over 1.5 million workers and 7 million payment tokens. The average end-to-end completion time is approximately 35.4 seconds, with zero gas cost. Compared to the state-of-the-art AVeCQ system [15], which requires about 13 minutes and 37 MWei per transaction on the Goerli network for only 1,024 users. The results confirm PTAP's efficiency, scalability, and strong privacy guarantees for large-scale mobile crowdsensing deployments.
Lorenzo Fogli, Alberto Montaner, Apostolos Kapetanios, Ioannis Mandourarakis · 8 authors
This paper presents the ENPOWER Flexibility Marketplace Toolkit, an open-source platform for peer-to-peer trading of energy flexibility within energy communities. Using energy consumption and production time-series data, participants can publish flexibility needs, submit offers, and verify delivery against measured baselines. Transactions are settled automatically through blockchain smart contracts with collateral enforcement, while an Energy Data Space backbone governs data exchange, ensuring sovereignty and policy-controlled sharing. Non-fungible tokens provide immutable, auditable certificates of each fulfilled flexibility commitment. The toolkit covers the complete trading lifecycle from market creation and participant onboarding through offer matching, delivery verification, and financial settlement.
Bitcoin's block reward is scheduled to decline to zero, raising concerns about whether the network can remain secure once miners rely solely on transaction fees. This paper seeks to identify the conditions under which large-scale and persistent deviation from honest mining can arise. We analyze and compare the payoffs of honest and deviating miners in a sequential decision model, and identify a deviation threshold $G_t$ at which honest mining ceases to be privately optimal. Around the 2024 Bitcoin halving, we show that current mining behavior does not exhibit large-scale or structural deviation. However, when the block reward is removed, the $G_t$ criterion implies that deviation can arise even with a very small fraction of transaction fees. Finally, we evaluate three protocol-level mechanisms: Base Fee, Fee Floor, and an adaptive maximum block size rule, and show that their combination raises the deviation threshold and mitigates incentive breakdown in a fee-only regime. These results provide a practical benchmark for assessing Bitcoin's security as block rewards disappear.
This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading strategy using the Exponential Moving Average (EMA) evaluated within a walk-forward procedure based on the Robust Sharpe Ratio is highly dependent on the chosen window size. We investigated the strategy on intraday Bitcoin data at six frequencies (1 minute to 60 minutes) using 81 combinations of walk-forward window lengths (1 day to 28 days) over a 19-month training period. The two best-performing parameter sets from the training data were applied to a 21-month out-of-sample testing period to ensure data independence. The strategy was only executed once during the testing period. To further validate the framework, strategy parameters estimated on Bitcoin were applied to Binance Coin and Ethereum. Our results suggest the robustness of our custom approach. In the training period for Bitcoin, all combinations of walk-forward windows outperformed a Buy-and-Hold strategy. During the testing period, the strategy performed similarly to Buy-and-Hold but with lower drawdown and a higher Information Ratio. Similar results were observed for Binance Coin and Ethereum. The real strength was demonstrated when a portfolio combining Buy-and-Hold with our strategies outperformed all individual strategies and Buy-and-Hold alone, achieving the highest overall performance and a 50 percent reduction in drawdown. A conservative fee of 0.1 percent per transaction was included in all calculations. A cost sensitivity analysis was performed as a sanity check, revealing that the strategy's break-even point was around 0.4 percent per transaction. This research highlights the importance of optimizing walk-forward window lengths and emphasizing the value of single-time out-of-sample testing for reliable strategy evaluation.
We develop a model of aberrant behavior by Bitcoin miners and test it with a new 2017-2025 dataset.Miners' rewards, comprised partly of user fees, exhibit variability across blocks of transactions.When large reward disparities exist between adjacent blocks, miners have incentives to attempt alternative versions of prior blocks and claim other miners' rewards for themselves.Regression analysis shows that fee differentials are associated with these attacks and longer waiting times between blocks.These patterns imply potential destabilization of the Bitcoin blockchain as future mining rewards become more volatile due to gradual withdrawal of fixed block subsidies.
With the continuous development of blockchain technology, its applications have expanded into a wide range of fields. The consensus algorithm serves as the core of blockchain, with its performance directly influencing the overall efficacy of the blockchain system. Delegated Proof of Stake (DPoS) selects block producers through elections and offers advantages such as high performance, low energy consumption, and strong scalability. However, the election mechanism also brings several challenges, including vote bribery, low voter participation, and high degree of centralisation. To address these issues, we propose an improved DPoS algorithm based on the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) decision-making method, named BKT-DPoS, which enhances the consensus mechanism from a new perspective of multi-attribute decision-making. Specifically, a dynamic balanced clustering algorithm is introduced to constrain the voting range of certain nodes; the voting results are transformed into node influence scores using complex network theory; and the historical performance of nodes is dynamically assessed based on block production outcomes. A TOPSIS model is constructed to select the final block producers by considering node influence and historical behaviour values as decision attributes. After each round, the behavioural values of nodes are updated to incentivise honest nodes and penalise malicious ones. We conducted extensive simulations on networks ranging from 200 to 5,000 nodes over 1,00 to 10,000 rounds, and performed a comparative analysis against other improved algorithms.Experimental results demonstrate that the proposed algorithm significantly improves decentralisation, enhances resistance to vote bribery, and effectively mitigates the impact of malicious nodes.
Nirmala Raju Kanti, D. G. Narayan, Pooja Shettar, P S Hiremath
Blockchain networks rely on consensus mechanisms to maintain security and efficiency. Delegated Proof of Stake (DPoS) is a widely adopted alternative to traditional Proof of Work (PoW) and Proof of Stake (PoS) due to its scalability. However, DPoS suffers from centralization risks, collusion, and the presence of unreliable validators, compromising network integrity. One approach to mitigating these issues is the downgrade method, which reduces the influence of dishonest validators over time by penalizing their stake or voting power. While this approach improves security, it still faces challenges related to manipulation and inefficiency. To address these limitations more effectively, we introduce Game Theory-based Delegated Proof of Stake (GT-DPoS), an advanced consensus framework that integrates strategic decision-making through game theory to optimize node selection and incentivization. GT-DPoS utilizes a two-stage evaluation mechanism based on Reputation Score (RS) and Trust Score (TS) to regulate node behavior, penalize malicious actors, and reward honest participation. In Stage 1, nodes are assessed based on RS, incorporating factors like stake, transaction efficiency, block contribution, and past misconduct. Nodes failing to meet the threshold are penalized, while eligible ones advance to second stage. Stage 2 evaluates TS, considering rewards, penalties, and community votes, refining the selection through a payoff-based model that ensures rational decision-making. Unlike the downgrade method, GT-DPoS provides a more dynamic and adaptive approach, ensuring continuous security enhancement without long-term inefficiencies. Experimental results demonstrate that GT-DPoS achieves faster block creation times compared to conventional DPoS, with up to 4.6% improvement at lower transaction loads and 1.2% at higher loads. By leveraging game-theoretic principles, GT-DPoS enhances decentralization, reduces transaction latency, and strengthens network security, making it a more effective and performance-optimized alternative to both traditional DPoS and downgrade-based approaches
Decentralized Autonomous Organizations (DAOs) are digital communities that make collective decisions by voting on blockchain communication-systems, amongst other coordination media. "Token voting" is intended to, on the one hand, efficiently synthesize individuals' limited information toward collective intelligence, on the other, it is presented as an inclusive approach to the governance of organizations-allowing any member with tokens to voice their opinions. In this latter sense, DAO decision-making is described as 'decentralized governance,' a misnomer, as most DAOs exhibit extreme centralization in token allocation. Yet, this paper finds value in DAO's collective decision-making intentions: the core blockchain infrastructure, when paired with a DAO's collective voting, produces an immutable, peerproduced ledger of group decision-making. Much empirical and theoretical attention has focused on consensus, understood as the majority-supported outcome of a vote. This paper argues that such a focus is incomplete. Consensus is often treated as a signal of a collective's certainty in a chosen outcome, yet this certainty depends on the structure of the decision space. The same majority share can imply different levels of certainty depending on the number of available options and how support is distributed across non-winning alternatives. As such, collective decisions cannot be evaluated by majority size alone, but must be understood relative to how a community allocates support across the full set of choices. To this end, this paper proposes the Rényi family of entropy-based measures, along with several derived metrics, as tools to map the topology of consensus and disagreement. It applies these metrics to voting data from 277 DAOs to signal (1) whether dissenting opinions converge into coordinated blocks of collective opposition or disperse pluralistically across the decision space, and (2) the effective number of choices and relative weight of substantively engaged alternatives within the choice set. The former heuristic qualifies the type of disagreement to ask "did disagreement converge into viable minority alternatives?" The latter heuristic quantifies the number of options that were seriously considered, and to what degree?" Altogether, this paper contributes a formal information-theoretic framework for representing the topology of collective decision-making through the distribution of support across all alternatives. It provides heuristics that treat a community's decision-making quality as a design question and enables majority outcomes to be interpreted relative to how support is distributed across the full set of choices, rather than in isolation.
Jonas Gehrlein, Grzegorz Miebs, Matteo Brunelli, Adam Mielniczuk · 5 authors
We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for maintaining the blockchain's physical infrastructure. The selection process is inherently subjective and multi-criterial and combines with the fact that nominators commonly operate through multiple accounts. This gives rise to a portfolio selection problem, where agents seek to distribute their nominations across accounts to diversify risk. We propose a decision support framework to optimize this selection by simultaneously maximizing two objectives: the expected utility of the validators likely to be allocated, representing portfolio quality and profitability, and the expected entropy of the allocation, representing diversification and risk mitigation across stashes. Validator utilities are derived using an original active preference learning procedure based on multi-attribute value theory, with emphasis on top-ranked validators. The resulting bi-objective optimization problem is solved with a multi-objective evolutionary algorithm and, to support the final choice, we introduce an interactive binary search navigation procedure that guides the nominator through the front and identifies a satisfactory trade-off with only a few questions. Numerical experiments examine the optimization strategies, while an expert assessment involving five experienced nominators confirms the approach's practical relevance and usefulness.
Yixuan Fan, Lei Zhang, Yao Sun, Xinyi Lin · 5 authors
The development of blockchain technology and the emergence of Web3 have given rise to a new paradigm known as Decentralized Autonomous Organizations (DAOs), online communities jointly owned and managed by members working for the same interests. Voting is the primary decision-making method within DAOs aligning with the decentralization philosophy. However, existing DAO voting mechanisms often exhibit a strong tendency toward centralized control, contradicting DAOs’ pursuit of decentralization. In the absence of a decentralization standard, we define the decentralization coefficient as a novel metric to evaluate the overall decentralization performance of DAO voting mechanisms quantitatively by establishing the first stochastic process model for the DAO voting process. By analyzing and simulating four typical voting mechanisms, we uncover that quorum and voting power thresholds, often thought to improve voting performance, may negatively impact decentralization. Additionally, the study analyzes the impact of three main factors, including voting power distribution, participation rate, and voting process, on decentralization. The findings highlight that decentralization is shaped by the interplay between these factors, rather than merely by adopting specific voting rules. This study provides a quantitative benchmark for future research and offers practical guidance for DAO designers, emphasizing the need to prioritize inclusive participation over additional voting conditions.