108,328 research outputs found
On the Influence of Informed Agents on Learning and Adaptation over Networks
Adaptive networks consist of a collection of agents with adaptation and
learning abilities. The agents interact with each other on a local level and
diffuse information across the network through their collaborations. In this
work, we consider two types of agents: informed agents and uninformed agents.
The former receive new data regularly and perform consultation and in-network
tasks, while the latter do not collect data and only participate in the
consultation tasks. We examine the performance of adaptive networks as a
function of the proportion of informed agents and their distribution in space.
The results reveal some interesting and surprising trade-offs between
convergence rate and mean-square performance. In particular, among other
results, it is shown that the performance of adaptive networks does not
necessarily improve with a larger proportion of informed agents. Instead, it is
established that the larger the proportion of informed agents is, the faster
the convergence rate of the network becomes albeit at the expense of some
deterioration in mean-square performance. The results further establish that
uninformed agents play an important role in determining the steady-state
performance of the network, and that it is preferable to keep some of the
highly connected agents uninformed. The arguments reveal an important interplay
among three factors: the number and distribution of informed agents in the
network, the convergence rate of the learning process, and the estimation
accuracy in steady-state. Expressions that quantify these relations are
derived, and simulations are included to support the theoretical findings. We
further apply the results to two models that are widely used to represent
behavior over complex networks, namely, the Erdos-Renyi and scale-free models.Comment: 35 pages, 8 figure
Evolution in Economic Geography: Institutions, Regional Adaptation and Political Economy
Economic geography has, over the last decade or so, drawn upon ideas from
evolutionary economics in trying to understand processes of regional growth and
change, with the concept of path dependence assuming particular prominence.
Recently, some prominent researchers have sought to delimit and develop an
evolutionary economic geography (EEG) as a distinct approach, aiming to create a
more coherent and systematic theoretical framework for research. This paper
contributes to debates on the nature and development of EEG. It has two main aims.
First, we seek to restore a broader conception of social institutions and agency to
EEG, informed by the recent writings of institutional economists like Geoffrey
Hodgson. Second, we link evolutionary concepts to political economy approaches,
arguing that the evolution of the economic landscape must be related to the broader
dynamics of capital accumulation, centred upon the creation, realisation and
geographical transfer of value. As such, we favour the utilisation of evolutionary and
institutional concepts within a geographical political economy approach rather than
the construction of a separate and theoretically âpureâ EEG; evolution in economic
geography, not an evolutionary economic geography
Collective intelligence: aggregation of information from neighbors in a guessing game
Complex systems show the capacity to aggregate information and to display
coordinated activity. In the case of social systems the interaction of
different individuals leads to the emergence of norms, trends in political
positions, opinions, cultural traits, and even scientific progress. Examples of
collective behavior can be observed in activities like the Wikipedia and Linux,
where individuals aggregate their knowledge for the benefit of the community,
and citizen science, where the potential of collectives to solve complex
problems is exploited. Here, we conducted an online experiment to investigate
the performance of a collective when solving a guessing problem in which each
actor is endowed with partial information and placed as the nodes of an
interaction network. We measure the performance of the collective in terms of
the temporal evolution of the accuracy, finding no statistical difference in
the performance for two classes of networks, regular lattices and random
networks. We also determine that a Bayesian description captures the behavior
pattern the individuals follow in aggregating information from neighbors to
make decisions. In comparison with other simple decision models, the strategy
followed by the players reveals a suboptimal performance of the collective. Our
contribution provides the basis for the micro-macro connection between
individual based descriptions and collective phenomena.Comment: 9 pages, 9 figure
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Exploring continuous organisational transformation as a form of network interdependence
In this paper we examine the problematic area of continuous transformation. We conduct our analysis from three theoretical perspectives: the resource based view, social network theory, and stakeholder theory. We found that the continuous transformation can be explained through the concept of Network Interdependence. This paper describes Network Interdependence and develops theoretical propositions from a synthesis of the three theories. Our contribution of Network Interdependence offers fresh insights into managing complex change and offers new ways of looking at organisational transformation
Social Learning over Weakly-Connected Graphs
In this paper, we study diffusion social learning over weakly-connected
graphs. We show that the asymmetric flow of information hinders the learning
abilities of certain agents regardless of their local observations. Under some
circumstances that we clarify in this work, a scenario of total influence (or
"mind-control") arises where a set of influential agents ends up shaping the
beliefs of non-influential agents. We derive useful closed-form expressions
that characterize this influence, and which can be used to motivate design
problems to control it. We provide simulation examples to illustrate the
results.Comment: To appear in 2017 in the IEEE Transactions on Signal and Information
Processing over Network
Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences
This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering
Doing evolution in economic geography
Evolutionary approaches in economic geography face questions about the relationships between their concepts, theories, methods, politics, and policy implications. Amidst the growing but unsettled consensus that evolutionary approaches should employ plural methodologies, the aims here are, first, to identify some of the difficult issues confronting those working with different frameworks. The concerns comprise specifying and connecting research objects, subjects, and levels; handling agency and context; engaging and integrating the quantitative and the qualitative; comparing cases; and, considering politics, policy, and praxis. Second, the purpose is to articulate a distinctive geographical political economy approach, methods, and illustrative examples in addressing these issues. Bringing different views of evolution in economic geography into dialogue and disagreement renders methodological pluralism a means toward improved understanding and explanation rather than an end in itself. Confronting such thorny matters needs to be embedded in our research practices and supported by greater openness; more and better substantiation of our conceptual, theoretical, and empirical claims; enhanced critical reflection; and deeper engagement with politics, policy, and praxis
Social Influence and the Collective Dynamics of Opinion Formation
Social influence is the process by which individuals adapt their opinion,
revise their beliefs, or change their behavior as a result of social
interactions with other people. In our strongly interconnected society, social
influence plays a prominent role in many self-organized phenomena such as
herding in cultural markets, the spread of ideas and innovations, and the
amplification of fears during epidemics. Yet, the mechanisms of opinion
formation remain poorly understood, and existing physics-based models lack
systematic empirical validation. Here, we report two controlled experiments
showing how participants answering factual questions revise their initial
judgments after being exposed to the opinion and confidence level of others.
Based on the observation of 59 experimental subjects exposed to peer-opinion
for 15 different items, we draw an influence map that describes the strength of
peer influence during interactions. A simple process model derived from our
observations demonstrates how opinions in a group of interacting people can
converge or split over repeated interactions. In particular, we identify two
major attractors of opinion: (i) the expert effect, induced by the presence of
a highly confident individual in the group, and (ii) the majority effect,
caused by the presence of a critical mass of laypeople sharing similar
opinions. Additional simulations reveal the existence of a tipping point at
which one attractor will dominate over the other, driving collective opinion in
a given direction. These findings have implications for understanding the
mechanisms of public opinion formation and managing conflicting situations in
which self-confident and better informed minorities challenge the views of a
large uninformed majority.Comment: Published Nov 05, 2013. Open access at:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.007843
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