Holistically Evaluating Agent Based Social System Models

The philosophical perspectives on model evaluation can be broadly classified into reductionist/logical positivist and relativist/holistic. In this paper, we outline some of our past efforts in, and challenges faced during, evaluating models of social systems with cognitively detailed agents. Owing to richness in the model, we argue that the holistic approach and consequent continuous improvement are essential to evaluating complex social system models such as these. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges, including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. We subscribe to the view that no model can faithfully represent reality, but detailed, descriptive models are useful in learning about the system and bringing about a qualitative jump in understanding of the system it attempts to model – provided they are properly validated. Our own approach to model evaluation is to consider the entire life cycle and assess the validity under two broad dimensions of (1) internally focused validity/quality achieved through structural, methodological, and ontological evaluations; and (2) external validity consisting of micro validity, macro validity, and qualitative, causal and narrative validity. In this paper, we also elaborate on selected validation techniques that we have employed in the past. We recommend a triangulation of multiple validation techniques, including methodological soundness, qualitative validation techniques, such as face validation by experts and narrative validation, and formal validation tests, including correspondence testing

Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud

Video analytics framework detection performance is worked at cloud. Object detection and classification are the basic tasks in video analytics and become the initial point for other complex submissions. Old fashioned video analytics approaches are manual and time consuming. These are particular due to the very participation of human factor. This paper present a cloud based video analytics framework for accessible and robust analysis of video streams. The framework enables an operative by programing the object detection and classification process from recorded video streams. An operative only specifies an analysis criteria and period of video streams to analyze. The streams are then realized from cloud storage, cracked and analyzed on the cloud. The framework performs compute severe parts of the analysis to CPU powered servers in the cloud. Vehicle and face finding are accessible as two case studies for assessing the framework, with one month of data and a 15 node cloud. The framework consistently performed object detection and classification on the data, comprising of 21,600 video streams and 175 GB in size, in 6.52 hours. The GPU enabled placement of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least double as fast than the cloud deployment Without GPUs. The analysis framework is high. R. Gnana bharathy "Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18906.pd

Validating Agent Based Social Systems Models

Validating social systems is not a trivial task. The paper outlines some of our past efforts in validating models of social systems with cognitively detailed agents. It also presents some of the challenges faced by us. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. Our own approach to validity assessment is to consider the entire life cycle and assess the validity under four broad dimensions of methodological validity, internal validity, external validity and qualitative, causal and narrative validity. In the past, we have employed a triangulation of multiple validation techniques, including face validation as well as formal validation tests including correspondence testing

Modeling the Personality & Cognition of Leaders

This paper summarizes efforts at adapting a personality profiling framework to model behavior and choices of political and military leaders. This is part of a larger project to create a role-playing, decision-making game to allow you to play out scenarios of interest against other leaders. In this modeling exercise we implement the Hermann leader personality profile tool to create historic leaders (Saladin, Richard I, etc.). We then attempt to validate the leader agents against scenarios of the 3rd Crusade

Profiling is Politically \u27Correct\u27: Agent-Based Modeling of Ethno-Political Conflict

A holy grail for military, diplomatic, and intelligence analysis is a valid set of software agent models that act as the desired ethno-political factions so that one can test the effects that may arise from alternative courses of action in different lands. This article enumerates the challenges of such a testbed and describes best-of-breed leader and follower profiling models implemented to improve the realism and validity of the agent. Realistic, \u27descriptive\u27 agents are contrasted to rational actor theory in terms of the different equilibria one would expect to emerge in conflict games. These predictions are examined in two real world cases (Iraq and SE Asia) where the agent models are subjected to validity tests and a policy experiment is then run. We conclude by arguing that substantial effort on game realism, best-of-breed social science models, and agent validation efforts is essential if analytic experiments are to effectively explore conflicts and alternative ways to influence outcomes. Such efforts are likely to improve behavioral game theory as well

What is a Good Pattern of Life Model? Guidance for Simulations

We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each

Modeling Factions for ‘Effects Based Operations’: Part II – Behavioral Game Theory

Military, diplomatic, and intelligence analysts are increasingly interested in having a valid system of models that span the social sciences and interoperate so that one can determine the effects that may arise from alternative operations (courses of action) in different lands. Part I of this article concentrated on internal validity of the components of such a synthetic framework – a world diplomacy game as well as the agent architecture for modeling leaders and followers in different conflicts. But how valid are such model collections once they are integrated together and used out-of-sample (see Section 1)? Section 2 compares these realistic, descriptive agents to normative rational actor theory and offers equilibria insights for conflict games. Sections 3 and 4 offer two real world cases (Iraq and SE Asia) where the agent models are subjected to validity tests and an EBO experiment is then run for each case. We conclude by arguing that substantial effort on game realism, best-of-breed social science models, and agent validation efforts is essential if analytic experiments are to effectively explore conflicts and alternative ways to influence outcomes. Such efforts are likely to improve behavioral game theory as well

Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations

Challenges of Country Modeling with Databases, Newsfeeds, and Expert Surveys

According to expert practitioners and researchers in the field of human behavior modeling ([Silverman et al., 2002; Pew and Mavor, 1998; Ritter et al., 2003]), a common central challenge now confronting designers of HBM (human-behavior-modeling) applications is to increase the realism of the synthetic agents\u27 behavior and coping abilities. It is well accepted in the HBM (human-behavior-modeling) community that cognitively detailed, thick models are required to provide realism. These models require that synthetic agents be endowed with cognition and personality, physiology, and emotive components. (We will hereafter refer to these rich models as cognitively detailed models or thick agents. ) To make these models work, one must find ways to integrate scientific know-how from many disciplines, and to integrate concepts and insights from hitherto fragmented and partial models from the social sciences, particularly from psychology, cultural studies, and political science. One consequence of this kind of integration of multiple and heterogeneous concepts and models is that we frequently end up with a large feature space of parameters that then need to be filled in with data