This happens in noisy settings when voters have limited information.

This happens in noisy settings when voters have limited information.

In representative democracies, the election of new representatives in regular election cycles is meant to prevent corruption and other misbehavior by elected officials and to keep them accountable in service of the ``will of the people." This democratic ideal can be undermined when candidates are dishonest when campaigning for election over these multiple cycles or rounds of voting. Much of the work on COMSOC to date has investigated strategic actions in only a single round. We introduce a novel formal model of \emph{pandering}, or strategic preference reporting by candidates seeking to be elected, and examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds. The two voting systems we compare are Representative Democracy (RD) and Flexible Representative Democracy (FRD). For each voting system, our analysis centers on the types of strategies candidates employ and how voters update their views of candidates based on how the candidates have pandered in the past. We provide theoretical results on the complexity of pandering in our setting for a single cycle, formulate our problem for multiple cycles as a Markov Decision Process, and use reinforcement learning to study the effects of pandering by both single candidates and groups of candidates across a number of rounds.

We introduce Flexible Representative Democracy (FRD), a novel hybrid of Representative Democracy (RD) and Direct Democracy (DD) in which voters can alter the issue-dependent weights of a set of elected representatives. In line with the literature on Interactive Democracy, our model allows the voters to actively determine the degree to which the system is direct versus representative. However, unlike Liquid Democracy, Flexible Representative Democracy uses strictly non-transitive delegations, making delegation cycles impossible, and maintains a fixed set of accountable, elected representatives. We present FRD and analyze it using a computational approach with issues that are binary and symmetric. We compare the outcomes of various voting systems using Direct Democracy with majority voting as an ideal baseline. First, we demonstrate the shortcomings of Representative Democracy in our model. We provide NP-Hardness results for electing an ideal set of representatives, discuss pathologies, and demonstrate empirically that common multi-winner election rules for selecting representatives do not perform well in expectation. To analyze the effects of adding flexibility, we begin by providing theoretical results on how issue-specific delegations determine outcomes. Finally, we provide empirical results comparing the outcomes of Representative Democracy, proxy voting with fixed sets of proxies across issues, and Flexible Representative Democracy with issue-specific delegations. Our results show that variants of Proxy Voting yield no discernible benefit over unweighted representatives and reveal the potential for Flexible Representative Democracy to improve outcomes as voter participation increases.

Science is in a Replication Crisis as the planet cooks. None of this is news. The viability of Representative Democracy has been in question since Plato, and probably for thousands of years before that. Science has always been a "look at me and my big brain" contest that led to shoddy methodologies and conclusions. And language has always just been a magic trick to convince yourself and others that truth can be wrung from the filthy dish cloth of reality by barking sounds and pointing.

In my last article here at Data Social we saw that it is very tricky to cluster European political parties based on the classic (and outdated?) Indeed, I found that although parties may belong to the same family they have different positions on important policies. Let's invoke together the power of machine learning and develop a better classification. In this article, using Principal Components Analysis (PCA), an unsupervised method, I find out where political parties really do belong in respect to each other's positions. Note: Check out this very cool article to learn more about what supervised and unsupervised methods are in the context of machine learning!

Direct democracy, where each voter casts one vote, fails when the average voter competence falls below 50%. This happens in noisy settings when voters have limited information. Representative democracy, where voters choose representatives to vote, can be an elixir in both these situations. We introduce a mathematical model for studying representative democracy, in particular understanding the parameters of a representative democracy that gives maximum decision making capability. Our main result states that under general and natural conditions, 1. for fixed voting cost, the optimal number of representatives is linear; 2. for polynomial cost, the optimal number of representatives is logarithmic.

Direct democracy, where each voter casts one vote, fails when the average voter competence falls below 50%. This happens in noisy settings when voters have limited information. Representative democracy, where voters choose representatives to vote, can be an elixir in both these situations. We introduce a mathematical model for studying representative democracy, in particular understanding the parameters of a representative democracy that gives maximum decision making capability. Our main result states that under general and natural conditions, 1. for fixed voting cost, the optimal number of representatives is linear; 2. for polynomial cost, the optimal number of representatives is logarithmic.

With the recent news about Facebook and Cambridge analytica, we are rightly concerned about the power and impact of algorithms to shape political debate and more generally, our lives. The social score model in China shows another way in which AI could influence all aspects of society. Based on these and other views, most policy makers in the West take a negative view of AI and the power of algorithms in society. In this post, I present a different, more optimistic view of the impact of AI on society where AI could be a part of the solution to overcome the problem of Algorithmocracy and filter bubbles. I discussed some of the ideas below Last week, I spoke at the Economist innovation summit in London.