Jennifer H. Watkins

Smartocracy: Social Networks for Collective Decision Making

Smartocracy is a social software system for collective decision making. The system is composed of a social network that links individuals to those they trust to make good decisions and a decision network that links individuals to their voted-on solutions. Such networks allow a variety of algorithms to convert the link choices made by individual participants into specific decision outcomes. Simply interpreting the linkages differently (e.g. ignoring trust links, or using them to weight an individuals vote) provides a variety of outcomes fit for different decision making scenarios. This paper can discuss the Smartocracy network data structures, the suite of collective decision making algorithms currently supported, and the results of two collective decisions regarding the design of the system

A Survey of Web-based Collective Decision Making Systems

A collective decision making system uses an aggregation mechanism to combine the input of individuals to generate a decision. The decisions generated serve a variety of purposes from governance rulings to forecasts for planning. The Internet hosts a suite of collective decision making systems, some that were inconceivable before the web. In this paper, we present a taxonomy of collective decision making systems into which we place seven principal web-based tools. This taxonomy serves to elucidate the state of the art in web-based collective decision making as well as to highlight opportunities for innovation.

Faith in the Algorithm, Part 2: Computational Eudaemonics

Eudaemonics is the study of the nature, causes, and conditions of human well-being. According to the ethical theory of eudaemonia, reaping satisfaction and fulfillment from life is not only a desirable end, but a moral responsibility. However, in modern society, many individuals struggle to meet this responsibility. Computational mechanisms could better enable individuals to achieve eudaemonia by yielding practical real-world systems that embody algorithms that promote human flourishing. This article presents eudaemonic systems as the evolutionary goal of the present day recommender system.

Using RDF to Model the Structure and Process of Systems

Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of

Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. Peter Csermely. (2006, Springer.) Hardcover,

10^9

64.95, 410 pages

edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.

Prediction Markets as an Aggregation Mechanism for Collective Intelligence

Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks. Peter Csermely. (2006, Springer.) Hardcover,

Grammar-based geodesics in semantic networks

64.95, 410 pages. Following Barabasi’s Linked [1], network science has become a popular and effective method to study the range of complex systems from molecular interactions to friendship networks. Many measures have been developed to classify and analyze networks. These structural metrics tend to focus on the more permanent connections of a network. Peter Csermely takes a different stance, focusing his book on those connections whose presence does not change the value of these measures in a ‘‘statistically discernible way’’ (p. 3)—the weak links. In contrast to the predominant emphasis on the more easily measured connections of a system, Weak Links focuses on the effervescent edges that make a network both dynamic and, in his opinion, stable. Csermely advocates a serious exploration of the stabilizing role of weak links in networks at all levels. Csermely posits the stabilizing effect of weak links to be a general network property of all complex systems. This ambitious claim is substantiated by varied examples from many disciplines as well as through intriguing conjecture in areas that require further research. With a surprising combination of scientific exposition and philosophical reflection, Weak Links opens with a biochemical research problem and takes the reader through the breadth of network science studies to conclude with a larger message for humankind. The first half of the book attempts to fully explain the components of Csermely’s hypothesis. This is time well spent, considering the notoriously difficult-to-define topics he tackles—stability, complexity, and weakness. Through a review of other accepted general network properties, such as scale-freeness, clustering, and preferential attachment, Csermely introduces the role of weak links, setting them in a familiar context. In what is anything but a typical review of network science, he includes such diverse asides as fractal geometry, Levy flights in biology, and the scale-free properties of music. What follows is the most technical chapter of the book, exploring the meaning of stability in networks. He concludes that weak links protect networks from perturbations that test network stability. Albeit a difficult subject, the stability definition tends toward the anecdotal. The second half of the book involves a trip to Netland. Here, each chapter is devoted to the description of weak link stabilization in networks of a particular scale. The journey begins with a review of the literature indicating weak link stabilization in macromolecules and continues to cells, organisms, and social networks with each successive chapter. The most unique work ensues as the trip to Netland continues to cultural networks (including language and architecture), the global web