Near Term Analysis: To generate “what-if” scenario a multi-agent model called “Near term Analysis” introduced. This framework generates “what-if” scenarios by processing the input dataset with the social network analysis method. To perform “what-if” analysis of an organization under different possible threat scenarios are done by using Multi-Agent system (MAS) called Dynet. This framework puts Dynet in data framing environment so that large number of simulations can be run with different possible threat scenarios.
Working of Near Term Analysis: The Near term analysis frame work takes meta-matrix as input and then the agent interaction mechanism takes place. The isolating of agents is the threat scenario that is
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Meta-matrix contains various kinds of nodes and internode type links. This network has sub-network such as agent-agent network, agent- knowledge network. By including these networks the interactions among the agents can be simulated. Illustrative example of Meta-matrix network is shown below Agents Knowledge Tasks
Agents Social Network Knowledge Network Assignment Network
Knowledge Information Network Needs Network
Tasks Precedence Ordering
Agent-Knowledge Network: The knowledge network is “who knows what” in organization. Knowledge is defined as different categories that are relevant to particular organization. For example, if we are collecting the data about organizational simulation group we may have the information like software development, organizational theory and statistics. The knowledge network is simply who possess what level of expertise in that particular field.
k1 k2 k3 k4
A1 1 0 0 1
A2 1 1 0 1
A3 1 0 0 1
A4 0 1 1 0 An Illustrative example for Agent-knowledge matrix .
“0” indicates that particular agent has no knowledge about the particular knowledge bit and “1” indicates otherwise. From above table we can say that A1 has knowledge about knowledge bit k1 and similarly A2 has no knowledge about the knowledge string k3.
Agent-interaction mechanism: The agents in this model have the opportunity to interact with others
Knowledge management defines the current use of the terms and identifies the core concept of managing knowledge in an organization (Atwood, 2009). The goal of Knowledge Management (KM) initiative is to improve the collective intelligence, or collective mind of the organizations and the resulting systematic coordination of knowledge ensures that the organization meets the customers’ needs (Quinn 1992, as cited in Maier 2004). According to the case study Langley Files, the company analyzed is the Central Intelligence Agency (CIA). The
A threat agent is a specific component that represents a danger to an organization’s assets. And a threat is an object, person or entity that represents a constant danger.
Network analysis is a visual representation of what needs to be done, and when it needs to be done. Networks are basically a technique to help management in the planning and control of projects. They also show relationships between the different tasks that need to be accomplished. Networks are not only useful for planning and controlling, but they can provide a means of communication between the various
The analysis is then given to consumers and policy makers, once it is checked by the analyst supervisor and peers. The analyst should also be ready to give a briefing on short notice. But both the analyst and the policy maker or consumer have to be aware of at all times, is that the intelligence field does not know everything. “On any given subject, the intelligence community faces what is in effect a field of rocks, and it lacks the resources to turn over every one to see what threats to national security may lurk underneath” (Pillar).
The position of knowledge management traffic cop is key to the rate at which an organization is able to learn. A single repository for the assembly and deployment of tools and applications is critical to the success of a KM program. Levinson also warns that KM is not just about a web portal, collaborative data base, or other electronic tools. It’s about Social Network Analysis (SNA) – how does knowledge thread its way through an organization, and does everyone speak the same language. Social Network Analysis is a diagnostic method for collecting and analyzing data about the patterns of relationships among people in groups. Applied to knowledge management, SNA can identify patterns of interaction in an enterprise, including its properties, such as the average number of links between people in an organization, the number and qualities of subgroups, information bottlenecks and knowledge brokers. SNA provides a view into the network of relationships that gives knowledge managers leverage to:
According to Lynch (2011), knowledge is known as a resource of the organization which has to be analyzed. The exploration of knowledge, helped in the creation of new knowledge, which opens up new opportunities for the organization. Knowledge does not only include data and information. It includes recorded information messages and discrete, observable facts about events that occur in an organization.
The concepts of knowledge and management have been around for a while. However, the combination of “knowledge management” is quite contemporary (Alvesson, 2001). It is widely accepted that knowledge is the most valuable resources in the modern organizations. Therefore, the sharing of knowledge in organizations is regarded as a significant way of making knowledge play a greater role in organizations. As a result, the capabilities to capture knowledge and manage knowledge have gained a great amount of attention gradually (Geiger, 2012). Accordingly, the occurrence of modern communication technologies, such as internet, intranet, e-mail and the world-wide web, allows for real-time interaction despite the physical distance, laying the foundation for the rapid development of KM (Hanson, 1999).
“Knowledge sharing means transfer, dissemination, and exchange of knowledge, experience, skills, and valuable information from one individual to other members within an organization”.
A multi-agent based system is a powerful modeling technique for simulating individual interactions in a dynamic system and is distinctive in its ability to simulate situations with unpredictable behavior
CE.2.2.4. At the initial stage, I asked my team to give their opinions about the tasks and also what were their technical strengths. I needed to know this to ensure we allocated tasks appropriately in order to finish the task on time. After several discussion for ideas contribution
Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. Agent-based modeling is a new analytical method for the social sciences, but one that is quickly becoming popular. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use
A knowledge base which is a combination of the knowledge and experience of the human experts
Knowledge management and information is the process of capturing, developing, sharing, and effectively using organizational knowledge.It refers to a multi-disciplinary approach to achieving organizational objectives by making the best use of knowledge. It includes the fields of business administration, information systems, management, library, and information sciences. Other fields may contribute to research, including information and media, computer science, public health, and public policy. Many large companies, public institutions, and non-profit organizations have resources dedicated to its efforts, often as a part of their business strategy, information technology, or human resource management departments.Several consulting companies provide advice regarding knowledge management to these organizations.
A knowledge is defined as a valuable intangible resource that need to be managed properly in order for an organization to gain advantage over their competitors, Birkinshaw and Sheehan (2002); Zyngier (2006). Knowledge transfer emphasized as “the movement of knowledge within the organization, it is a distinct experience not a gradual process of dissemination, and depends on the characteristics of everyone involve”, Szulanski (1996).
Knowledge management has been developed in priority and increased popularity as a research topic from the era of 20Th century. There was enough term for many organizations to introduce KM methods and KM systems. Computing and information systems of an organization are used as a base for implementing knowledge based management systems.KM features mainly on knowledge development, inquisition, refinement, utilization and storage. Knowledge management systems commonly involve the use of databases in the current 21st century and many challenges involve directly related, mixed activities that pursue laws of life and social sciences that affirm science with engineering. The models of knowledge management