In a time of ecological and social crises, designing new institutions is crucial for meeting future challenges. Institutions emerge from the imaginations of individuals, moving into social movements and crystallizing in legal structures. As present movements seek to develop tomorrow’s governances, they turn to decentralized structures: distributed networks with little hierarchy, characterized by a diversity of actors and dynamics. While movements and technologies may seek to utilize decentralized ideas, they lack design principles: only recently has it been possible to decentralize at the national stage. Designs may come from biology and ecological networks; fungi, plants, and slime molds in soil to the neurons, glia, and blood vessels within our own bodies. This article blends these biological analogies with present technologies like blockchain and Web3 to posit future decentralized institutions, movements and legalities that give greater consideration to the more-than-human world.
Karen Cunningham, David J. Anderson, Brandon Weissbourd
Jellyfish comprise a diverse clade of free-swimming predators that arose prior to the Cambrian explosion. They play major roles in ocean ecosystems via a suite of complex foraging, reproductive, and defensive behaviors. These behaviors arise from decentralized, regenerative nervous systems composed of body parts that generate the appropriate part-specific behaviors autonomously following excision. Here, we discuss the organization of jellyfish nervous systems and opportunities afforded by the recent development of a genetically tractable jellyfish model for systems and evolutionary neuroscience.
Christian Nedu Osakwe, Oluwatobi A. Ogunmokun, Islam Elgammal, Darya Baeva · 5 authors
Abstract This article adopts the valueâattitudeâbehavioural (VAB) and attitudeâbehaviourâcontext (ABC) theoretical lenses to develop an integrative model to examine attitudinal and behavioural responses to cryptocurrency investment. It also investigates the moderating role of generational differences (preâmillennials vs. millennials). The study showed that perceived value is closely associated with the attitude towards cryptocurrency investment which, in turn, is strongly associated with the willingness to make and recommend cryptocurrency investments. Results further reveal that contextual factors such as convertibility and sugrophobia, which reflect the fear of being duped, strongly influence individuals' willingness to recommend cryptocurrency investments to others. Finally, results indicate that generational differences play an important moderating role.
Abstract The stated aim of cryptocurrencies is to free the monetary system from the need to trust financial intermediaries, by relying on incentive design and technology. Many descriptive studies, however, have questioned cryptocurrenciesâ delivery on the promise of trustlessness. This paper promotes a normative analysis of trust in cryptocurrencies by discussing (i) whether trust is in principle eliminable, and (ii) whether trustlessness is in itself a desirable goal. These issues are closely related, we argue, to the further issue of what kind of institutions cryptocurrencies represent. We discuss the cognitive functions played by cryptocurrencies through the lens of the âextended mindâ hypothesis in the philosophy of mind and hence conceive of cryptocurrencies as mind-extending institutions. As the models of institutional mind extension differ in the fiduciary bond they assume exists between individuals and institutional resources, we compare the reliance-based model of âscaffolding institutionsâ with the trust-based model of âcognitive institutions,â showing that the ineliminability and desirability of trust lead to seeing cryptocurrencies as instances of the latter. In the end, our discussion suggests that trust is a necessary component of cryptocurrenciesâ cognitive functions and its promotion helps to perform such cognitive functions more effectively and sustainably
Antonio Carovilla, Remo Pareschi, Francesco Salzano
Swarm robotics is a field that studies the design and coordination of large groups of robots that can perform complex tasks through collective behaviors. One of the challenges in swarm robotics is to find the optimal balance between decentralization and centralization, as each approach has its advantages and disadvantages. In this paper, we propose a semi-centralized framework that integrates blockchain technology for enhanced coordination and security in swarm robotic systems. Our framework uses a centralized control unit that serves as a coordination hub for the robotic swarm while also leveraging blockchain technology to provide a secure and distributed ledger for data storage and communication. Thus, blockchain technology mitigates the reduced resilience, which is the price to pay for introducing semi-centralized control in robotic swarms, by ensuring the distributed management of data and programs. In the paper, we exemplify the application of our semi-centralized framework to optimize a path-finding problem for a swarm of robots. We also characterize a series of levels in allocating the blockchain infrastructure to cope with the corresponding levels of security threats.
Stefan Kitzler, Stefano Balietti, Pietro Saggese, Bernhard Haslhofer · 5 authors
We present a study analyzing the voting behavior of contributors, or vested users, in Decentralized Autonomous Organizations (DAOs). We evaluate their involvement in decision-making processes, discovering that in at least 7.54% of all DAOs, contributors, on average, held the necessary majority to control governance decisions. Furthermore, contributors have singularly decided at least one proposal in 20.41% of DAOs. Notably, contributors tend to be centrally positioned within the DAO governance ecosystem, suggesting the presence of inner power circles. Additionally, we observed a tendency for shifts in governance token ownership shortly before governance polls take place in 1202 (14.81%) of 8116 evaluated proposals. Our findings highlight the central role of contributors across a spectrum of DAOs, including Decentralized Finance protocols. Our research also offers important empirical insights pertinent to ongoing regulatory activities aimed at increasing transparency to DAO governance frameworks.
Sebastian MĂŒller, Isabel Amigo, Alexandre Reiffers-Masson, Santiago Ruano-RincĂłn
In directed acyclic graph (DAG)-based distributed ledgers, unreferenced blocks (tips) form the backlog of a distributed queueing system. Each new block creates one tip and attempts to remove up to $k$ existing tips by referencing them. With heterogeneous propagation delays, these service decisions are made from delayed local information, so nodes may disagree on the backlog and some reference attempts are wasted. We study a continuous-time Poisson model with bounded heterogeneous delays and uniform tip selection. We prove that the embedded tip-configuration chain is irreducible, aperiodic, and positive Harris recurrent, and hence admits a unique stationary regime. The observer and local tip-pool sizes have stationary exponential moments, converge to their stationary limits, and satisfy almost-sure ergodic averages. We also derive a Little-type identity relating the stationary mean observer tip count to the mean time until a typical block is first referenced. Simulations are included as qualitative illustrations of the effects of delay variability and issuance heterogeneity.
Open access
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math.PR
cs.DC
Mathematical and Theoretical Epidemiology and Ecology Models
Dark forest (DF) is the first successful decentralized real-time strategy (RTS) game. Based on smart contracts, the game explores the application possibilities of new technologies, and prefigures the operation of the social system in the metaverse era. In this study, we collect on-chain data from DF, map player behavior, and discuss playersâ game asset management, combat strategies in extreme mode, and the gameâs exit mechanism. We believe that blockchain games, such as DF, can be viewed as a new technological space with metaverse characteristics, in which individuals art free to challenge centralized social structures using decentralized technology while subjected to more secret postmodern social surveillance.
Marco Alberto Javarone, Gabriele Di Antonio, Gianni Valerio Vinci, L. Pietronero · 5 authors
The energy sustainability of blockchains, whose consensus protocol rests on the Proof-of-Work, nourishes a heated debate. The underlying issue lies in a highly energy-consuming process, defined as mining, required to validate crypto-asset transactions. Mining is the process of solving a cryptographic puzzle, incentivised by the possibility of gaining a reward. The higher the number of users performing mining, i.e. miners, the higher the overall electricity consumption of a blockchain. For that reason, mining constitutes a negative environmental externality. Here, we study whether miners' interests can meet the collective need to curb energy consumption. To this end, we introduce the Crypto-Asset Game, namely a model based on the framework of Evolutionary Game Theory devised for studying the dynamics of a population whose agents can play as crypto-asset users or as miners. The energy consumption of mining impacts the payoff of both strategies, representing a direct cost for miners and an environmental factor for crypto-asset users. The proposed model, studied via numerical simulations, shows that, in some conditions, the agent population can reach a strategy profile that optimises global energy consumption, i.e. composed of a low density of miners. To conclude, can a Proof-of-Work-based blockchain become energetically sustainable? Our results suggest that blockchain protocol parameters could have a relevant role in the global energy consumption of this technology.
In this paper I employ a mixed methods approach in an effort to study a novel family of case studies in human collectivization - DAOs (Decentralized Autonomous Organizations). Born out of the blockchain ecosystem and the sociology of the internet, these organizations greatly overlap with common pool resource systems and common goods systems studied in traditional literature. Under this lens, DAOs are found to overcome problems of cooperation by utilizing algorithmic governance. Six case studies are discussed, and specific designs are examined with regards to the tendency of the members to free ride. Ultimately, DAOs are found to be an immature - yet promising, blueprint for the future of human cooperation, fully compatible and relevant to the work of Samuel Olson and Elinor Ostrom.
Reducing energy consumption is crucial not only to reduce OPEX but also to reduce the human debt to our planet. Over the past few years, most service providers (SPs) have actively tackled this issue, particularly targeting periods of low activity. Indeed, having fewer customers during these periods allows SPs to downsize or shut down part of their infrastructure. But this is not always optimal. Despite multiple energy-efficient optimizations, a mobile national operator (MNO) still need to maintain significant radio access network (RAN) infrastructure active at night. Could MNOs do better by cooperating with each other in such a way that an MNO can redirect its subscribers to a partner MNO, thus allowing its entire infrastructure to be temporarily deactivated while switching roles with the partner during a subsequent drop in activity period? To answer this question, we investigated a novel collaborative framework based on multi-agent reinforcement learning (MARL) allowing for negotiations between SPs as well as trustful reports from a distributed ledger technology (DLT) to evaluate the amount of energy saved. We leveraged it to experiment three different sets of rules (free, recommended, or imposed) regulating the negotiation between multiple SPs (3, 4, 8, or 10). Based on the observation of four cooperation metrics (efficiency, safety, incentive-compatibility, and fairness), the simulations showed that the imposed set of rules proved to be the best mode.
We develop an analysis of the cryptocurrency market borrowing methods and concepts from ecology. This approach makes it possible to identify specific diversity patterns and their variation, in close analogy with ecological systems, and to characterize the cryptocurrency market in an effective way. At the same time, it shows how non-biological systems can have an important role in contrasting different ecological theories and in testing the use of neutral models. The study of the cryptocurrencies abundance distribution and the evolution of the community structure strongly indicates that these statistical patterns are not consistent with neutrality. In particular, the necessity to increase the temporal change in community composition when the number of cryptocurrencies grows, suggests that their interactions are not necessarily weak. The analysis of the intraspecific and interspecific interdependency supports this fact and demonstrates the presence of a market sector influenced by mutualistic relations. These latest findings challenge the hypothesis of weakly interacting symmetric species, the postulate at the heart of neutral models.
The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.
A decentralized blockchain is a distributed ledger that is often used as a platform for exchanging goods and services. This ledger is maintained by a network of nodes that obeys a set of rules, called a consensus protocol, which helps to resolve inconsistencies among local copies of a blockchain. In this paper, we build a mathematical framework for the consensus protocol designer, specifying (a) the measurement of a resource which nodes strategically invest in and compete for to win the right to build new blocks in the blockchain; and (b) a payoff function for such efforts. Thus, the equilibrium of an associated stochastic differential game can be implemented by selecting nodes in proportion to this specified resource and penalizing dishonest nodes by its loss. This associated, induced game can be further analyzed using mean field games. The problem can be broken down into two coupled PDEs, where an individual node's optimal control path is solved using a Hamilton-Jacobi-Bellman equation, and where the evolution of states distribution is characterized by a Fokker-Planck equation. We develop numerical methods to compute the mean field equilibrium for both steady states at the infinite time horizon and evolutionary dynamics. As an example, we show how the mean field equilibrium can be applied to the Bitcoin blockchain mechanism design. We demonstrate that a blockchain can be viewed as a mechanism that operates in a decentralized setup and propagates properties of the mean field equilibrium over time, such as the underlying security of the blockchain.
Blockchains have witnessed widespread adoption in the past decade in various fields. The growing demand makes their scalability and sustainability challenges more evident than ever. As a result, more and more blockchains have begun to adopt proof-of-stake (PoS) consensus protocols to address those challenges. One of the fundamental characteristics of any blockchain technology is its crypto-economics and incentives. Lately, each PoS blockchain has designed a unique reward mechanism, yet, many of them are prone to free-rider and nothing-at-stake problems. To better understand the ad-hoc design of reward mechanisms, in this paper, we develop a reward mechanism framework that could apply to many PoS blockchains. We formulate the block validation game wherein the rewards are distributed for validating the blocks correctly. Using evolutionary game theory, we analyze how the participants' behaviour could potentially evolve with the reward mechanism. Also, penalties are found to play a central role in maintaining the integrity of blockchains.
In the blockchain network, to get rewards in the blockchain, blockchain participants pay for various forms of competition such as computing power, stakes, and other resources. Because of the need to pay a certain cost, individual participants cooperate to maintain the longâterm stability of the blockchain jointly. In the course of such competition, the game between each other has appeared invisibly. To better understand the blockchain design of cooperation mechanisms, in this paper, we constructed a game framework between participants with different willingness, using evolutionary game theory, and complex network games. We analyzed how the behavior of participants potentially develops with cost and payoff. We consider the expected benefits of participants for the normal growth of the blockchain as the major factor. Considering the behavior of malicious betrayers, the blockchain needs to be maintained in the early stage. Numerical simulation supports our analysis.
The rise of blockchain has led to discussions on new governance models and the cooperation of multiple participants. Due to the cognitive defects of the blockchain protocol in terms of intelligent contracts and decentralized autonomous organizations (DAOs), it is often unclear as to how to make decisions about the evolution of blockchain applications. Many autonomous organizations, with the support of network technologies such as blockchain, blindly absorb members and expand the scale of the capital pool, while ignoring the cost advantage of traditional autonomous organizations based on social relations and mutual supervision to fight information asymmetry. In this context, this study analyzes the evolutionary trend of autonomous organizations and their membersâ strategies under different policy environments. To this end, under the digital economy background, based on game theory, the evolutionary dynamics method, and the form of the mutual insurance organization, this study constructs an evolutionary dynamics model of distributed autonomous organizations. The results show that blind expansion without review aggravates the overall risk poolâs moral hazard, in the context of mutual insurance. Organizational strategies, such as risk pool splits, can effectively improve the risk poolâs operating performance and establish a benign competition elimination mechanism. Driven by cooperation efficiency and split supervision based on homogeneous clustering, the comprehensive application of the market elimination mechanism can effectively combat moral hazards, restrain the adverse effects of member flow, expand the living space of smallâ and mediumâsized insurance organizations, curb the emergence of a largeâscale monopoly risk pool, and improve market vitality. These conclusions and suggestions also apply to autonomous organizations based on social relations and mutual supervision. The results offer specific decisionâmaking guidance and suggestions for the government, insurance companies, and risk management.
Open access
Evolutionary Game Theory and Cooperation
Mathematical and Theoretical Epidemiology and Ecology Models