A comparison of zero- and minimal-intelligence agendas in majority-rule voting models
Journal of Economic Interaction and Coordination - Trang 1-35 - 2023
Tóm tắt
Emergent behavior in repeated collective decisions of minimally intelligent agents—who at each step in time invoke majority rule to choose between a status quo and a random challenge—can manifest through the long-term stationary probability distributions of a Markov chain. We use this known technique to compare two kinds of voting agendas: a zero-intelligence agenda that chooses the challenger uniformly at random and a minimally intelligent agenda that chooses the challenger from the union of the status quo and the set of winning challengers. We use Google Co-Lab’s GPU accelerated computing environment to compute stationary distributions for some simple examples from spatial-voting and budget-allocation scenarios. We find that the voting model using the zero-intelligence agenda converges more slowly, but in some cases to better outcomes.
Tài liệu tham khảo
Aldrich JH, Alt JE, Lupia A (eds) (2007). Positive changes in political science: the legacy of Richard D. McKelvey’s most influential writings. University of Michigan Press, Ann Arbor
Arrow KJ (1950) A difficulty in the concept of social welfare. J Polit Econ 58(4):328–346
Aseere AM, Gerding EH, Millard DE (2010) A voting-based agent system for course selection in e-learning. In: 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, IEEE, vol. 2, pp. 303–310
Avin C, Lotker Z, Mizrachi A, Peleg D (2019) Majority vote and monopolies in social networks. In: Proceedings of the 20th international conference on distributed computing and networking, pp 342–351
Barshan B (2001) Ultrasonic surface profile determination by spatial voting. In: IMTC 2001. Proceedings of the 18th IEEE instrumentation and measurement technology conference. Rediscovering measurement in the age of informatics (Cat. No.01CH 37188), vol 1, pp 583–588. Doi: https://doi.org/10.1109/IMTC.2001.928885
Beeson MJ (1992) Triangles with vertices on lattice points. Am Math Mon 99(3):243–252. https://doi.org/10.2307/2325060
Black D (1948) On the rationale of group decision-making. J Polit Econ 56(1):23–34
Brewer PJ, Huang M, Nelson B, Plott CR (2002) On the behavioral foundations of the law of supply and demand: human convergence and robot randomness. Exp Econ 5(3):179–208
Condorcet JAMNC, et al. (1785) Essai sur l’application de l’analyse à la probabilité des décisions, rendues à la pluralité des voix/| c par m. le marquis de condorcet
Conitzer V, Sandholm T (2003) Universal voting protocol tweaks to make manipulation hard. arXiv: cs/0307018
Cox GW, Shepsle KA (2007) Majority cycling and agenda manipulation: Richard McKelvey’s contributions and legacy. In: Aldrich JH, Alt JE, Lupia A (eds) Positive changes in political science: the legacy of Richard D. McKelvey’s most influential writings. University of Michigan Press, Ann Arbor
Dougherty KL, Edward J (2005) A nonequilibrium analysis of unanimity rule, majority rule, and pareto. Econ Inq 43(4):855–864
Dougherty KL, Edward J (2012) Voting for pareto optimality: a multidimensional analysis. Public Choice 151(3):655–678
Duggan J (2005) A survey of equilibrium analysis in spatial models of elections. https://www.sas.rochester.edu/psc/duggan/papers/existsurvey4.pdf
Duggan J, Schwartz T (2000) Strategic manipulability without resoluteness or shared beliefs: Gibbard-satterthwaite generalized. Soc Choice Welf 17(1):85–93
EU-Parliament (2016) Regulation (EU) 2016/679 of the European Parliament and of the council. Regulation (Eu) 679:2016
Farsa DZ, Bidgoli AA, Rokhsat-Yazdi E, Rahnamayan S (2021) Population-based coordinate descent algorithm with majority voting. In: Proceedings of the genetic and evolutionary computation conference companion, Association for computing machinery, New York, USA, GECCO ’21, pp. 1283-1289. Doi: https://doi.org/10.1145/3449726.3463186,
Ferejohn JA, McKelvey RD, Packel EW (1984) Limiting distributions for continuous state Markov voting models. Soc Choice Welf 1(1):45–67
Friedrich T, Kötzing T, Krejca MS, Nallaperuma S, Neumann F, Schirneck M (2016) Fast building block assembly by majority vote crossover. In: Proceedings of the genetic and evolutionary computation conference, pp. 661–668
Gibbard A (1973) Manipulation of voting schemes: a general result. Econometrica 41(4):587–601
Gibbard A (1977) Manipulation of schemes that mix voting with chance. Econometrica 45(3):665–681
Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J Polit Econ 101(1):119–137
Gode DK, Sunder S (1993b) Lower bounds for efficiency of surplus extraction in double auctions. In: The double auction market institutions, theories, and evidence, Proceedings of the workshop on double auction markets. Santa Fe, New Mexico, pp. 199–219
Gode DK, Sunder S (1997) What makes markets allocationally efficient? Q J Econ 112(2):603–630
Grinstead C, Snell J, Doyle P (2006) Introduction to probability. https://math.dartmouth.edu/~prob/prob/prob.pdf
Gross DJ (1996) The role of symmetry in fundamental physics. Proc Natl Acad Sci 93(25):14256–14259
Häggström O (2002) Finite Markov chains and algorithmic applications. Cambridge University Press, Cambridge
Hayek F (1945) The use of knowledge in society. Am Econ Rev 35(4):519–530
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York
Kalandrakis A (2004) A three-player dynamic majoritarian bargaining game. J Econ Theory 116(2):294–322. https://doi.org/10.1016/S0022-0531(03)00259-X
Tomayko JE (1988) Computers in the space shuttle avionics system. https://history.nasa.gov/computers/Ch4-4.html
Kubernetes blog contributors (2016) Simple leader election with kubernetes and docker. https://kubernetes.io/blog/2016/01/simple-leader-election-with-kubernetes/
Levin DA, Peres Y, Wilmer E (2009) Markov chains and mixing times. American Mathematical Soc, Providence
Levine ME, Plott CR (1977) Agenda influence and its implications. Virginia Law Rev 63(4):561–604
McKelvey RD (1976) Intransitivities in multidimensional voting models and some implications for agenda control. J Econ Theory 12(3):472–482
McKelvey RD, Ordeshook PC et al (1990) A decade of experimental research on spatial models of elections and committees. In: Enelow JM, Hinich MJ (eds.) Advances in the spatial theory of voting. Cambridge University Press, Cambridge, pp 99–144. https://doi.org/10.1017/CBO9780511896606.007
Numpy Developers (2006) Numpy: the fundamental package for scientific computing with Python (version 1.19.5). https://pypi.org/project/numpy/1.19.5/, see also https://numpy.org/
Oikonomopoulos A, Patras I, Pantic M (2010) Discriminative space-time voting for joint recognition and localization of actions. In: Proceedings of the 2nd international workshop on social signal processing. Association for computing machinery, New York, SSPW ’10, pp. 11-16. Doi: https://doi.org/10.1145/1878116.1878122,
O’Neil C (2016) Weapons of math destruction: how big data increases inequality and threatens democracy. Broadway Books, New York
Ordeshook P (1993) The development of contemporary political theory. In: Barnett WA, Hinich MJ, Schofield N (eds) Political economy: institutions, competition, and representation. Proceedings of the seventh international symposium in economic theory and econometrics, chap. 4. Cambridge University Press, New York
Ordeshook P (2007) The competitive solution revisited. In: Aldrich JH, Alt JE, Lupia A (eds) Positive changes in political science: the legacy of Richard D. McKelvey’s most influential writings. University of Michigan Press, Ann Arbor
Parsons R, Truran J (1970) 206. Equilateral triangles on geoboards. Math Gaz 54(387):53–54. https://doi.org/10.2307/3613163
Paul TK, Iba H (2009) Prediction of cancer class with majority voting genetic programming classifier using gene expression data. IEEE/ACM Trans Comput Biol Bioinf 6(2):353–367. https://doi.org/10.1109/TCBB.2007.70245
Penn EM (2009) A model of farsighted voting. Am J Polit Sci 53(1):36–54
Plott C (1967) A notion of equilibrium and its possibility under majority rule. Am Econ Rev 57(4):787–806
Plott CR (1994) Symposium: market architectures, institutional landscapes and testbed experiments: introduction. Econ Theor 4(1):3–10
Preferred Networks Inc (2015) Cupy: Numpy and scipy for gpu (version 9.1.0). https://pypi.org/project/cupy/9.1.0/, see also https://cupy.dev/
Raschka S (2015) Python machine learning. Packt publishing ltd, Birmingham
Brewer P (2021) Python module “gridvoting” (version 0.9.3). https://pypi.org/project/gridvoting/0.9.3/
Satterthwaite MA (1975) Strategy-proofness and arrow’s conditions: existence and correspondence theorems for voting procedures and social welfare functions. J Econ Theory 10(2):187–217. https://doi.org/10.1016/0022-0531(75)90050-2
Schofield N (1978) Instability of simple dynamic games. Rev Econ Stud 45(3):575–594. https://doi.org/10.2307/2297259
Schwartz T (1982) No minimally reasonable collective-choice process can be strategy-proof. Math Soc Sci 3(1):57–72. https://doi.org/10.1016/0165-4896(82)90006-3
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423
Smith VL (1962) An experimental study of competitive market behavior. J Polit Econ 70(2):111–137
Teixeira M, d’Orey PM, Kokkinogenis Z (2018) Autonomous vehicles coordination through voting-based decision-making. In: International conference on agreement technologies. Springer, pp. 199–207
Teixeira M, d’Orey PM, Kokkinogenis Z (2020) Simulating collective decision-making for autonomous vehicles coordination enabled by vehicular networks: a computational social choice perspective. Simul Model Pract Theory 98:101983
Tseitlin A (2013) The antifragile organization. Commun ACM 56(8):40–44. https://doi.org/10.1145/2492007.2492022
Tullock G (1967) The general irrelevance of the general impossibility theorem. Q J Econ 81(2):256–270
Xu Y, Deng Z, Wang M, Xu W, So AMC, Cui S (2020) Voting-based multiagent reinforcement learning for intelligent IoT. IEEE Internet Things J 8(4):2681–2693
Yu L, Wang S, Lai KK (2009) An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring. Eur J Oper Res 195(3):942–959