Socionics: Sociological Concepts for Social Systems of Artificial (and Human) Agents

Socionics is an interdisciplinary approach with the objective to use sociological knowledge about the structures, mechanisms and processes of social interaction and social communication as a source of inspiration for the development of multi-agent systems, both for the purposes of engineering applications and of social theory construction and social simulation. The approach has been spelled out from 1998 on within the Socionics priority program funded by the German National research foundation. This special issue of the JASSS presents research results from five interdisciplinary projects of the Socionics program. The introduction gives an overview over the basic ideas of the Socionics approach and summarizes the work of these projects.Socionics, Sociology, Multi-Agent Systems, Artificial Social Systems, Hybrid Systems, Social Simulation

Differential Equation Models Derived from an Individual-Based Model Can Help to Understand Emergent Effects

We study a model of primacy effect on individual's attitude. Typically, when receiving a strong negative feature first, the individual keeps a negative attitude whatever the number of moderate positive features it receives afterwards. We consider a population of individuals, which receive the features from a media, and communicate with each other. We observe that interactions favour the primacy effect, compared with a population of isolated individuals. We derive a differential equation system ruling the evolution of probabilities that individuals retain different sets of features. The study of this aggregated model of the IBM shows that interaction can increase or decrease the number of individuals exhibiting a primacy effect. We verify on the IBM that the interactions can decrease the primacy effect in the conditions suggested by the study of the aggregated model. We finally discuss the interest of such a double-modelling approach (using a model of the individual based model) for this application.Primacy Effect, Information Filtering, Agent-Based Model, Aggregated Model, Collective Effects of Interactions, Double-Modelling

The Cowl - v.10 - n.11 - Feb 11, 1948

The Cowl - student newspaper of Providence College. Volume 10, Number 11 - Feb 11, 1948. 6 pages

A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations

The Cowl - v.11 - n.10 - Feb 10, 1949

The Cowl - student newspaper of Providence College. Volume 11, Number 10 - February 10, 1949. 4 pages

Pioneer 10 and 11

The DSN (Deep Space Network) mission support requirements for Pioneer 10 and 11 are summarized. The primary objective of these Pioneer missions is to investigate the interplanetary medium beyond the orbit of Saturn and, in particular, to gather data which may locate the heliopause as these spacecraft cruise out of the solar system to the extreme of their communication capabilities. The mission objectives are outlined and the DSN support requirements are defined through the presentation of tables and narratives describing the spacecraft flight profile; DSN support coverage; frequency assignments; support parameters for telemetry, command and support systems; and tracking support responsibility

The Santa Clara, 2018-10-11

https://scholarcommons.scu.edu/tsc/1076/thumbnail.jp

"Did the 2008 Rebate Fail? A Response to Taylor and Feldstein"

Did the 2008 rebate fail to stimulate consumer spending? In their influential AER articles, John Taylor and Martin Feldstein each claim that BEA aggregate time series data show that the 2008 rebate failed. Re-examining the BEA data, we find that the data instead show there is a high probability that the rebate stimulated consumption. Moreover, the hypothesis that a rebate has half the impact of ordinary disposable income cannot be rejected. Thus, we find that analysis of the BEA aggregate time series data is consistent with the conclusion from the micro-data studies that the 2008 rebate stimulated consumer spending.fiscal policy, fiscal stimulus, tax rebates

Risk Shocks and Housing Markets

This paper analyzes the role of uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production. The analysis demonstrates that risk shocks to the housing production sector are a quantitatively important impulse mechanism for the business cycle. Also, we demonstrate that bankruptcy costs act as an endogenous markup factor in housing prices; as a consequence, the volatility of housing prices is greater than that of output, as observed in the data. The model can also account for the observed countercyclical behavior of risk premia on loans to the housing sector.agency costs, credit channel, time-varying uncertainty, residential investment, housing production, calibration