Resolution of Blockchain Conflicts through Heuristics-based Game Theory and Multilayer Network Modeling

A blockchain is a fully distributed system in which the user behavior, actions and decisions are crucial for its operation. This paper discusses how to handle conflict situations affecting a blockchain system. Specifically, we model two real-world conflict scenarios -- the Lazy Miner dilemma and the Impatient Seller dilemma -- by proposing a novel multi-layer framework coupled with a heuristics-based game-theoretic modeling. The multi-layer approach provides a way to include cross-modality integration (human quality factors, such as reliability) and human actions on the blockchain. We design a multi-agent game-theoretic methodology combined with some statistical estimators derived from the heuristics. Our model also includes the concept of homophily, a human-related factor connected to the similarity and frequency of interactions on the multi-layer network. Based on the heuristics, a dynamically evolving measure of weights is further defined such that an agent increases or decreases the link weights to its neighbours according to the experienced payoffs. We show how data mining in blockchain data could be incorporated into a heuristic model which provides parameters for the game-theoretic payoff matrix. Thus, this work represents a platform for simulating the evolutionary dynamics of the agents' behaviors, including also heuristics and homophily on a multi-layer blockchain network.

[1]  Pietro Liò,et al.  Quantifying the propagation of distress and mental disorders in social networks , 2018, Scientific Reports.

[2]  Zibin Zheng,et al.  Blockchain challenges and opportunities: a survey , 2018, Int. J. Web Grid Serv..

[3]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[4]  Vito Latora,et al.  The new challenges of multiplex networks: Measures and models , 2016, The European Physical Journal Special Topics.

[5]  Fran Casino,et al.  A systematic literature review of blockchain-based applications: Current status, classification and open issues , 2019, Telematics Informatics.

[6]  Pietro Liò,et al.  Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory , 2015, PloS one.

[7]  Toby Murray,et al.  Empirically Analyzing Ethereum's Gas Mechanism , 2019, 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

[8]  Vito Latora,et al.  Collective Phenomena Emerging from the Interactions between Dynamical Processes in Multiplex Networks. , 2014, Physical review letters.

[9]  Paolo Tasca,et al.  Blockchain Technologies: The Foreseeable Impact on Society and Industry , 2017, Computer.

[10]  Khaled Salah,et al.  IoT security: Review, blockchain solutions, and open challenges , 2017, Future Gener. Comput. Syst..

[11]  Pietro Liò,et al.  Improving QoE in Multi-layer Social Sensing: A Cognitive Architecture and Game Theoretic Model , 2019, SocialSens@CPSIoTWeek.

[12]  Nicolas Courtois,et al.  On Subversive Miner Strategies and Block Withholding Attack in Bitcoin Digital Currency , 2014, ArXiv.

[13]  Bhaskar Krishnamachari,et al.  Solving the Buyer and Seller’s Dilemma: A Dual-Deposit Escrow Smart Contract for Provably Cheat-Proof Delivery and Payment for a Digital Good without a Trusted Mediator , 2018, 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC).

[14]  Emin Gün Sirer,et al.  Majority is not enough , 2013, Financial Cryptography.

[15]  Daniel Davis Wood,et al.  ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER , 2014 .

[16]  G. Szabó,et al.  Evolutionary games on graphs , 2006, cond-mat/0607344.

[17]  Pietro Liò,et al.  Social dynamics modeling of chrono-nutrition , 2019, PLoS Comput. Biol..

[18]  Laura Ricci,et al.  A blockchain based approach for the definition of auditable Access Control systems , 2019, Comput. Secur..

[19]  Tim Roughgarden,et al.  Incentive Compatibility of Bitcoin Mining Pool Reward Functions , 2016, Financial Cryptography.

[20]  Laura Ricci,et al.  Data-driven analysis of Bitcoin properties: exploiting the users graph , 2018, International Journal of Data Science and Analytics.

[21]  Edward L. Ionides,et al.  Plug-and-play inference for disease dynamics: measles in large and small populations as a case study , 2009, Journal of The Royal Society Interface.

[22]  P. Todd,et al.  Simple Heuristics That Make Us Smart , 1999 .

[23]  George Drosatos,et al.  Blockchain Applications in the Biomedical Domain: A Scoping Review , 2019, Computational and structural biotechnology journal.

[24]  Ittay Eyal,et al.  The Miner's Dilemma , 2014, 2015 IEEE Symposium on Security and Privacy.

[25]  Joshua A. Kroll,et al.  The Economics of Bitcoin Mining, or Bitcoin in the Presence of Adversaries , 2013 .

[26]  Ghassan O. Karame,et al.  Two Bitcoins at the Price of One? Double-Spending Attacks on Fast Payments in Bitcoin , 2012, IACR Cryptol. ePrint Arch..

[27]  Ying-Chang Liang,et al.  A Survey on Blockchain: A Game Theoretical Perspective , 2019, IEEE Access.

[28]  Pietro Liò,et al.  Combining evolutionary game theory and network theory to analyze human cooperation patterns , 2016 .

[29]  Pietro Liò,et al.  Human Heuristics for Autonomous Agents , 2007, BIOWIRE.