What Can Political Science Learn from Computer Science?

2017 ◽  
Author(s):  
Derek Ruths
Keyword(s):  
Computer Science ◽  
Political Science ◽  
Network Science ◽  
Political Networks ◽  
Science Learn

This chapter is one of six in this book in which a leading scholar from another field is interviewed about network science. These scholars bring to bear the perspective and experience of their fields, as well as insights about how network science integrates with questions that tend to be of interest to political scientists. They have knowledgeable and unique perspectives on the potential intellectual contributions that political scientists can make using network science approaches. In this chapter, Derek Ruths describes how computer science has contributed to, and learned from, applications of political networks emphasizing areas where collaborations between scholars of these fields are likely to be most fruitful.

What Can Political Science Learn from Business and Management?

2017 ◽  
Author(s):  
Steven Borgatti
Keyword(s):  
Political Science ◽  
Network Science ◽  
Political Networks ◽  
Science Learn ◽  
Business And Management

This chapter is one of six in this book in which leading scholars from other fields are interviewed about network science. These scholars bring to bear the perspective and experience of their fields, as well as insights about how network science integrates with questions that tend to be of interest to political scientists. They have knowledgeable and unique perspectives on the potential intellectual contributions that political scientists can make using network science approaches. This interview brings perspectives from the field of business and management to the study of political networks. Importantly, Stephen Borgatti has been a leader in the field in terms of developing applications and demonstrating productive application of network tools to questions of relevance. In this interview he brings these insights to political science.

What Can Political Science Learn from Sociology?

2017 ◽  
Author(s):  
James Moody
Keyword(s):  
Political Science ◽  
Network Science ◽  
Science Learn

This chapter is one of six in this book in which a leading scholar from another field is interviewed about network science. These scholars bring to bear the perspective and experience of their fields, as well as insights about how network science integrates with questions that tend to be of interest to political scientists. They have knowledgeable and unique perspectives on the potential intellectual contributions that political scientists can make using network science approaches.

What Can Political Science Learn from Economics?

2017 ◽  
Author(s):  
Matthew Jackson
Keyword(s):  
Political Science ◽  
Network Science ◽  
Science Learn

This chapter is one of six in this book in which a leading scholar from another field is interviewed about network science. These scholars bring to bear the perspective and experience of their fields, as well as insights about how network science integrates with questions that tend to be of interest to political scientists. They have knowledgeable and unique perspectives on the potential intellectual contributions that political scientists can make using network science approaches.

What Can Political Science Learn from Statistics and Psychology?

2017 ◽  
Author(s):  
Stanley Wasserman
Keyword(s):  
Political Science ◽  
Network Science ◽  
Science Learn

This chapter is one of six in this book in which a leading scholar from another field is interviewed about network science. These scholars bring to bear the perspective and experience of their fields, as well as insights about how network science integrates with questions that tend to be of interest to political scientists. They have knowledgeable and unique perspectives on the potential intellectual contributions that political scientists can make using network science approaches.

What Can Political Science Learn from Mathematics?

2017 ◽  
Author(s):  
Peter Mucha
Keyword(s):  
Political Science ◽  
Network Science ◽  
Science Learn

This chapter is one of six in this book in which a leading scholar from another field is interviewed about network science. These scholars bring to bear the perspective and experience of their fields, as well as insights about how network science integrates with questions that tend to be of interest to political scientists. They have knowledgeable and unique perspectives on the potential intellectual contributions that political scientists can make using network science approaches.

What can computer science learn from a fine arts approach to teaching?

2005 ◽  
Vol 37 (1) ◽  
pp. 421-425 ◽  
Author(s):  
Lecia J. Barker ◽  
Kathy Garvin-Doxas ◽  
Eric Roberts
Keyword(s):  
Computer Science ◽  
Fine Arts ◽  
Approach To Teaching ◽  
Science Learn

Threshold Phenomena and Influence: Perspectives from Mathematics, Computer Science, and Economics

2005 ◽  
Author(s):  
Gil Kalai ◽  
Shmuel Safra
Keyword(s):  
Computer Science ◽  
Political Science ◽  
Monotone Function ◽  
Majority Voting ◽  
Simple Majority ◽  
Condorcet’S Jury Theorem ◽  
Threshold Phenomenon ◽  
Threshold Phenomena ◽  
Aggregation Of Information ◽  
Jury Theorem

Threshold phenomena refer to settings in which the probability for an event to occur changes rapidly as some underlying parameter varies. Threshold phenomena play an important role in probability theory and statistics, physics, and computer science, and are related to issues studied in economics and political science. Quite a few questions that come up naturally in those fields translate to proving that some event indeed exhibits a threshold phenomenon, and then finding the location of the transition and how rapid the change is. The notions of sharp thresholds and phase transitions originated in physics, and many of the mathematical ideas for their study came from mathematical physics. In this chapter, however, we will mainly discuss connections to other fields. A simple yet illuminating example that demonstrates the sharp threshold phenomenon is Condorcet's jury theorem, which can be described as follows. Say one is running an election process, where the results are determined by simple majority, between two candidates, Alice and Bob. If every voter votes for Alice with probability p > 1/2 and for Bob with probability 1 — p, and if the probabilities for each voter to vote either way are independent of the other votes, then as the number of voters tends to infinity the probability of Alice getting elected tends to 1. The probability of Alice getting elected is a monotone function of p, and when there are many voters it rapidly changes from being very close to 0 when p < 1/2 to being very close to 1 when p > 1/2. The reason usually given for the interest of Condorcet's jury theorem to economics and political science [535] is that it can be interpreted as saying that even if agents receive very poor (yet independent) signals, indicating which of two choices is correct, majority voting nevertheless results in the correct decision being taken with high probability, as long as there are enough agents, and the agents vote according to their signal. This is referred to in economics as asymptotically complete aggregation of information.

The Oxford Handbook of Political Networks

2016 ◽  
Keyword(s):  
Political Science ◽  
Network Theory ◽  
The State ◽  
Social Worlds ◽  
Political Networks ◽  
Terrorist Networks ◽  
Key Questions ◽  
State Of The Field

This volume is a foundational resource on the study of networks in politics. It is grounded in the understanding that networks are omnipresent in the natural and social worlds, and they are at the heart of politics. The 43 essays in this volume offer instruction on network theory and methods at beginning and advanced levels; they also provide an assessment of the state-of-the-discipline on a variety of applied network topics in politics. Leading scholars in the field address key questions in political science such as: Why do people vote? How can governments foster innovations? How can countries build ties that promote peace? What are the most fruitful strategies for disrupting arms or terrorist networks? In answering these questions, the volume provides both a summary of the state of the field and a roadmap for moving forward.

scholarly journals Understanding complex systems: When Big Data meets network science

2015 ◽  
Vol 57 (4) ◽  
Author(s):  
Ingo Scholtes
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Complex Systems ◽  
Computer Science ◽  
Network Science ◽  
Grand Challenge ◽  
Research Activities

AbstractBetter understanding and controlling complex systems has become a grand challenge not only for computer science, but also for the natural and social sciences. Many of these systems have in common that they can be studied from a network perspective. Consequently methods from network science have proven instrumental in their analysis. In this article, I introduce the macroscopic perspective that is at the heart of network science. Summarizing my recent research activities, I discuss how a combination of this perspective with Big Data methods can improve our understanding of complex systems.

scholarly journals Systematically Understanding Cybersecurity Economics: A Survey

2021 ◽  
Vol 13 (24) ◽  
pp. 13677
Author(s):  
Mazaher Kianpour ◽  
Stewart J. Kowalski ◽  
Harald Øverby
Keyword(s):  
Literature Review ◽  
Computer Science ◽  
Political Science ◽  
Decision Makers ◽  
Digital Ecosystems ◽  
Academic Field ◽  
Specific Research ◽  
Complex Problems ◽  
Dynamic Stochastic ◽  
Research Questions

Insights in the field of cybersecurity economics empower decision makers to make informed decisions that improve their evaluation and management of situations that may lead to catastrophic consequences and threaten the sustainability of digital ecosystems. By drawing on these insights, cybersecurity practitioners have been able to respond to many complex problems that have emerged within the context of cybersecurity over the last two decades. The academic field of cybersecurity economics is highly interdisciplinary since it combines core findings and tools from disciplines such as sociology, psychology, law, political science, and computer science. This study aims to develop an extensive and consistent survey based on a literature review and publicly available reports. This review contributes by aggregating the available knowledge from 28 studies, out of a collection of 628 scholarly articles, to answer five specific research questions. The focus is how identified topics have been conceptualized and studied variously. This review shows that most of the cybersecurity economics models are transitioning from unrealistic, unverifiable, or highly simplified fundamental premises toward dynamic, stochastic, and generalizable models.

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