Collective Decision Making Systems: From the Ideal State to Human Eudaimonia
Upcoming SlideShare
Loading in...5
×
 

Collective Decision Making Systems: From the Ideal State to Human Eudaimonia

on

  • 2,336 views

Few scholastic disciplines have within them an explicit ideal beyond the production of knowledge. With computer science and engineering, the implicit ideal is to ensure better living through ...

Few scholastic disciplines have within them an explicit ideal beyond the production of knowledge. With computer science and engineering, the implicit ideal is to ensure better living through circuitry. Personally, my motivation is driven by the sense that social algorithms will lead to a greater human experience.

Statistics

Views

Total Views
2,336
Views on SlideShare
2,330
Embed Views
6

Actions

Likes
1
Downloads
37
Comments
0

1 Embed 6

http://www.slideshare.net 6

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Collective Decision Making Systems: From the Ideal State to Human Eudaimonia Collective Decision Making Systems: From the Ideal State to Human Eudaimonia Presentation Transcript

  • Collective Decision Making Systems: From the Ideal State to Human Eudaimonia Marko A. Rodriguez T-5, Center for Nonlinear Studies Los Alamos National Laboratory http://markorodriguez.com February 13, 2009
  • Collaborators • Jennifer H. Watkins Collective Decision Making Systems Los Alamos National Laboratory International and Applied Technology Los Alamos National Laboratory http://public.lanl.gov/jhw • Alberto Pepe Center for Embedded Networked Sensing University of California at Los Angeles http://albertopepe.com http://cdms.lanl.gov External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Why do we do the things we do? • Few scholastic disciplines have within them an explicit ideal beyond the production of knowledge. • With computer science and engineering, the implicit ideal is to ensure better living through circuitry. • Personally, my motivation is driven by the sense that social algorithms will lead to a greater human experience. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • The Eighteenth Century’s Age of Enlightenment • Citizen’s began to question the traditional forms of aristocratic and monarchic governance and began to envision an ideal state. • The United States was the social experiment to achieve this ideal state. • Unfortunately, the ideals of these thinkers could not reach their purest forms due to the limitations of the technology at the time. Moreover, as a people we should value the ideals, not the implementation of government.1 Marquis de Condorcet Thomas Paine Adam Smith 1 Marko A. Rodriguez and Jennifer H. Watkins, “Revisiting the Age of Enlightenment from a Collective Decision Making Systems Perspective”, LA-UR-09-00324, February 2009. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Marquis de Condorcet (French: 1743 – 1794) Social Choice Theory 1.0 0.8 0.6 p 0.4 0.2 0.0 0 10 20 30 40 50 60 70 80 90 100 n • Suppose a group of n decision makers under a two option, majority rule vote, where each decision maker has probability p of choosing the optimal option. • If p > 0.5 and as n → ∞, then the probability of yielding the optimal decision approaches 1.0. • if p < 0.5 and as n → ∞, then the probability of yielding the optimal decision approaches 0.0. • Condorcet’s Jury Theorem is considered the first non-ethical justification for democratic governance2 . 2 Marquis de Condorcet, “Essai sur l’Application de l’Analyse aux Probabilit´s des Decisions prises ` la Pluralit´ des Voix”, e a e 1785. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Thomas Paine (English: 1737 – 1809) Representation – Ensuring a Large Population • Ardent supporter of the American Revolution. His passion was driven primarily by his ideal of self-governance.3 • When a population is small, “some convenient tree will afford them a State house.” • As the population increases in size, representatives must “act in the same manner as the whole body would act were they present.” 3 Thomas Paine, “Common Sense”, 1776. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Dynamically Distributed Democracy • Imagine a government architecture where there is no a priori established power structure: no president, no representatives, no senators. • Imagine an Internet-based, fraud-proof, direct democratic, decision making system. • Problem: there are too many decisions and not enough time in a citizen’s day to participate in all decision making processes. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • citizen’s color denotes their “political tendency”, where full red is 0, full blue citizen in this population, where xi is the is 1, and purple is 0.5. The layout algorithm chosen is the Fruchterman- en i and, for the purpose of simulation, is Reingold layout. a uniform distribution. Assume that every tion of n citizens uses some social network- create links to those individuals that they 1. Let y ∈ Rn denote the total amount of vote power that has + Dynamically ir tendency the best. In practice, these links Distributed Democracy of the algorithm. Finally, flowed to each citizen over the course ose friend, a relative, or some public figure a ∈ {0, 1}n x ∈ [0, 1]: votercitizen i is participating (ai = 1) • denotes whether tendency. endencies resonate with the individual. In in the current decision : making process or not (ai = 0). The • A ∈ Rn×n the social network. resentatives are any citizens, not political + values of a are biased by an unfair coin that has probability k ve in public office. Let A ∈ [0, 1]n×n denote • a ∈ {0, 1}n: k-percent participation. of making the citizen an active participant and 1−k of making presenting the network, where the weight of • y ∈ Rn : received vote power. the citizen inactive. The iterative algorithm is presented below, + urpose of simulation, is denoted xj = 0.5 where ◦ denotes Rn : propagated vote power. • π ∈ entry-wise multiplication and ≈ 1. + 1 − |xi − xj | if link exists = π←0 0 otherwise. i≤n while i=1 yi < do y ← y + (π ◦ a) inked citizens arei = 1.0 x identical in their political π ← π ◦ (1 − a) strength of the link is 1.0. If their tendencies π ← Aπ posing, then their trust (and the strength of end Note that a preferential attachment network is used to generate a degree distribution that xk = 0.0 ical social networks “in the wild” (i.e. scale- In words,Collective tendency: y ·as vote power “sinks” in • active citizens serve x Moreover, an assortativity parameter is used that once•they receive of y ·power, from themselves or from The round vote x is the collective vote. ctions in the network towards citizens with a neighbor in the network, they do not pass it on. Inactive . The assumption here is that given a system citizens serve as vote power “sources” in that they propagate s more likely for citizens to create links to their vote Presentation – Losthe network links to their neighbors External Advisory Committee power over Alamos, New Mexico – February 13, 2009 dividuals than to those whose opinions are iteratively until all (or ) vote power has reached active e resultant link matrix A is then normalized citizens. At this point, the tendency in the active population ic in order to generate a probability distribu- is defined as δ tend = x · y. Figure 4 plots the error incurred
  • Dynamically Distributed Democracy 0.50 0.60 0.70 0.80 0.90 1.00 0.20 dynamically distributed democracy dynamically distributed democracy proportion of correct decisions direct democracy direct democracy 0.15 i≤n error 1 0.10 error = xi − (x · y) n i=1 0.05 0.00 100 90 80 70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 percentage of active citizens (n) percentage of active citizens (n) • A parameter k ∈ [0, 1] denotes the percentage of citizens that are actively participating. • Any subset of the whole can be made to behave as the whole. In other words, “act in the same manner as the whole body would act were they present.” External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Adam Smith (Scottish: 1723 – 1790) Self Interested Actors - Ensuring an Enlightened Majority • When a citizen pursues “his own interest he frequently promotes that of the society more effectually than when he really intends to promote it”.4 • Market mechanisms are not only useful for determining commodity prices as they can be generally applied to information aggregation. 4 Adam Smith, “An Inquiry in the Nature and Causes of the Wealth of Nations”, 1776. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Decision Markets • A decision market functions because it guarantees a return on investment for quality information. • A decision market is a tool for attracting a population of knowledgeable citizens much like a commodity market is a tool for attracting knowledgeable speculators. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Decision Markets [1,1,1] e [0.7,0.6,0.7] • e ∈ {1}d: the environment. • m ∈ [0, 1]d: the market. [0.5,0.5,0.4] [0.7,0.6,0] • red path: incentive-free market. • blue path: incentive market. q 1 Pi≤d • Market accuracy: √ d i=1 (mi − ei)2. [0,0,0.4] [0.5,0,0.4] • Rounding each dimension of m yields the m collective decision. [0,0,0] [0.7,0,0] External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Decision Markets 1.0 1.0 incentive market proportion of correct decisions i≤d incentive-free market 1 0.8 0.8 error = √ (mi − ei )2 d i=1 0.6 0.6 error 0.4 0.4 0.2 0.2 incentive market incentive-free market 0.0 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 average citizen knowledge (p) average citizen knowledge (p) • Incentives in decision making ensure a thoughtful contribution of knowledge. • Moreover, it ensures participation from those who have knowledge of the domain. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Artistotle (Greek: 384 – 322 B.C.) Eudaimonia – Ensuring a Virtuous Citizenry • Being virtuous is repeatedly choosing correctly. • Habitual correct behavior leads to the ultimate, objective goal of life: eudaimonia – complete engagement in the world, doing what you do because nothing else matters.5,6 • Can systems aid citizens in choosing correctly – in all aspects of life? Aristotle 5 Aristotle, “Nicomachean Ethics”, 350 B.C. 6 Mihaly Csikszentmihalyi, “Flow: The Psychology of Optimal Experience”, Harper Perennial, 1990. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Recommendation Systems But if the development of character is a the moral objective, it is obvious that [...] the choices of vocation and avocations to pursue, of friends to cultivate, of books to read are moral for they clearly influence such development.7 • Web services are continuing to build richer models of humans, resources, and the relationships between them. • There exists an increasing reliance on such services to aid in decision making: correct books (Amazon.com), correct movies (NetFlix.com), correct music (Pandora), correct occupation (Monster.com), correct friends (PointsCommuns.com), correct life partner (Match.com), etc. 7 David L. Norton, “Democracy and Moral Development: A Politics of Virtue”, University of California Press, 1991. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Grammar-Based Random Walkers • Algorithms can search multi-relational data structures in a way that biases towards the requirements of the problem domain?8 What venue should I submit this article to? Who is the best person to peer-review this article? Who should I talk to at this conference and what should I talk to them about? Conference attending Person sponsors attending attending editorOf ? ? Person Person Journal affiliation read authored containedIn Institution Document cites Document 8 Marko A. Rodriguez, “Grammar-Based Random Walkers”, Knowledge-Based Systems, 21(7), pp. 727-739, 2008. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009
  • Conclusion • Thomas Jefferson stated that the purpose of a government is to ensure “life, liberty, and the pursuit of happiness.” • Are we as a society still too myopic to take on the bigger task of ensuring life, liberty, and the guarantee of eudaimonia? • This is the only reason why we should do the things we do.9 • Collective decision making systems offer solutions to this age old problem.10 9 Marko A. Rodriguez and Alberto Pepe, “Faith in the Algorithm, Part 1: Beyond the Turing Test”, 2008. 10 Jennifer H. Watkins and Marko A. Rodriguez, “A Survey of Collective Decision Making Systems”, in Studies in Computational Intelligence: Evolution of the Web in Artificial Intelligence Environments, Springer-Verlag, pp. 245–279, 2008. External Advisory Committee Presentation – Los Alamos, New Mexico – February 13, 2009