In this landmark book, Scott Page redefines the way we understand ourselves in relation to one another. The Difference is about how we think in groups--and how our collective wisdom exceeds the sum of its parts. Why can teams of people find better solutions than brilliant individuals working alone? And why are the best group decisions and predictions those that draw upon the very qualities that make each of us unique? The answers lie in diversity--not what we look like outside, but what we look like within, our distinct tools and abilities. The Difference reveals that progress and innovation may depend less on lone thinkers with enormous IQs than on diverse people working together and capitalizing on their individuality. Page shows how groups that display a range of perspectives outperform groups of like-minded experts. Diversity yields superior outcomes, and Page proves it using his own cutting-edge research. Moving beyond the politics that cloud standard debates about diversity, he explains why difference beats out homogeneity, whether you're talking about citizens in a democracy or scientists in the laboratory. He examines practical ways to apply diversity's logic to a host of problems, and along the way offers fascinating and surprising examples, from the redesign of the Chicago "El" to the truth about where we store our ketchup. Page changes the way we understand diversity--how to harness its untapped potential, how to understand and avoid its traps, and how we can leverage our differences for the benefit of all.

Prologue to The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies

Computational systems and intelligent machines are facing with both compositional and environmental growing forms of complexity. We contribute a way to better deal with these forms of complexity by proposing a novel agent-based software platform: Knowledge request-Broker Architecture (KnoRBA). This solution aims at easily implementing self-organizing distributed software systems, mainly composed of socially interactive software agents. KnoRBA features a better way for heterogeneous agents to achieve mutual understanding: instead of specifying a protocol, it equips agents with programmatic means to provide knowledge to, and query it from the society. Utilization of KnoRBA is made possible by provision of appropriate language extensions and code libraries. This article, having reviewed the efficiencies and deficiencies of the existing literature, describes the basic principles, system architecture, and Java-based implementation of KnoRBA, as well as

Knowledge Request-Broker Architecture: A Platform for Social Computational Intelligence

It has already been realized by the scientific and technical community that a new form of technology is going to lead the future technological developments. This technology will be more humancentric and will be more and more “hidden” within everyday-life objects. It will be smarter, personalized, pervasive and ubiquitous. This technology includes what is called Ambient Intelligence (AmI). In this paper, we identify the main aspects of AmI through a review of the recent developments that have been achieved in these aspects of AmI and Ambient Intelligence Environments (AmIEs), as well as point out the problems yet to be solved and the visions of the future.

/pdf/identifying-aspects-of-ambient-intelligence-through-a-review-57iozzskjv.pdf

Identifying aspects of Ambient Intelligence through a review of recent developments

By thinking of a society as a cognitive system, this paper proposes a theoretical framework for artificial social intelligence. This framework is implemented using Knowledge Request-Broker Architecture, and its applicability is experienced in the field of system identification by letting a society of self-interested agents, with various skills, to identify a system based on given training data. To develop a theory about how social intelligence works, two questions were asked. First, “What was changed in time that made less intelligent societies to become more intelligent?”, and second “what drives individuals to take part in social productiveness?”. The framework reported in this paper is inspired by the answers given to these question through the study of the emergence of human civilizations, and putting it beside computer and knowledge engineering concepts. The experiments with this system show how social ensemble of simple autonomous agents can solve harder problems, while demonstrating the practicality of the proposed framework and its associated API.

Synthetic design of a social regressor and its implementation using Knowledge Request-Broker Architecture

A-Cell is a high-level abstraction of fine-grained parallelism specifically designed to be applicable to all range of parallel devices from super computers based on CPUs or GPUs, to network of embedded devices. To achieve this, A-Cell adopts a programming model called "connectionist computing" and with that takes a leap step away from Turing programming model. Also, in contrast with most common solutions like PGAS that are holistic, the philosophy of A-Cell is reductionist. An ACell encapsulates a fine-grained task with its related variables. A source-to-source compiler translates the program to a set of programs that are compilable to the target devices. Execution of the task is through massive instantiation of an A-Cell prototype. The runtime system takes the responsibility of distributing A-Cell instances between all available nodes, cores or multiprocessors (MPs). The runtime system also assures synchronization and consistency between A-Cell instances. This paper introduces the theoretical aspect of A-Cell, its semantics and its design logic, and reports the progress made for its materialization, including the A-Cell simulator, C/C++ runtime system, and precompiler.

http://ieeexplore.ieee.org/iel7/6949414/6949433/06949482.pdf

Introducing A-Cell for Scalable and Portable SIMD Programming

Artificial social intelligence is about making better intelligent systems out of a group of cooperating less intelligent problem solver agents. Better intelligence can be achieved by having expert agent with diverse expertise cooperating and working on the same problem. For that purpose a platform is required, which, in one hand, provides the freedom for developers to implement any form of solution in their agents, and in the other hand, facilitates mutual understanding between heterogeneous agents by standardizing the way they represent and inquire knowledge. While must published works are concerned with one or the other, the Knowledge Request Broker (KRB) system reported in this paper supports both requirements, and is made specifically for intelligent agents. This paper proposes an architecture for KRB, and provides a practical example for its usage in solving curve-fitting problems.

A Knowledge Request-Broker Architecture for Development of Artificial Social Intelligence

In the progress of computing and network technologies, the amount of information in demand-oriented information services is being increased. Therefore, it is required to reduce the size of data and to provide continuous services. Additionally, users have heterogeneous service level requirements, but it is difficult for conventional centralized information system to meet the above needs simultaneously. Autonomous Decentralized Information System (ADIS) is proposed to achieve these requirements. In this system, the layered data is employed and each data is distributed on different levels. Therefore, the system can provide service with adaptability and minimum total storage volume. In this paper, ADIS is implemented as streaming video service and to satisfy the heterogeneous quality requirements, high and low service levels are shown for simplicity. As for the high service levels, the redundant hot-standby node mechanism is utilized, however, this is not provided for low service level because it may cause congestion in the whole system. To achieve continuous service for low service level without decreasing high's service level, the reservation backup approach is utilized. This is used for multiple users per slot to increase service continuity. Additionally, it is required to reserve nodes that are vacant within minimum steps as quick as possible. However, there is a trade-off between the node's congestion and the search area. To solve above problems, the autonomous assurance technology is proposed. In this technology, a mobile agent decides the scope of the search, concerning the congestion in the local area. Moreover, in the failure of a node, the recovery process will proceed automatically. The effectiveness of this technology is proved through implementation of practical streaming system.

Autonomous assurance technology for service continuity in demand-oriented information system

Sociomimetic Computing, as described in this paper, is a computing paradigm inspired by social systems in which computer software is made out of socially cooperative agents as its basic components. To contrast with social simulation which is a computational method for analysis of social system, this paradigm is about synthesizing social systems for computational purposes. This paper describes a framework for this paradigm, and explains Social Regression an example of its practical implementation. It also provides an overview on Knowledge-Request Broker Architecture, which is the platform made to support sociomimetic computing. Being one of the first papers written on this paradigm, it also draws some trends for its future.

http://ieeexplore.ieee.org/iel5/6045373/6060171/06060523.pdf

Hamed Khandan

Papers

Knowledge Request-Broker Architecture: A Platform for Social Computational Intelligence

Synthetic design of a social regressor and its implementation using Knowledge Request-Broker Architecture

A Knowledge Request-Broker Architecture for Development of Artificial Social Intelligence

Autonomous assurance technology for service continuity in demand-oriented information system

Framework, platform and trends for sociomimetic computing