A Summary of "Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model"

Citation
R. Sun and I. Naveh, Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation, Vol.7, No.3. 2004.

Summary / Assessment
In their paper, Sun and Naveh argue that social simulation needs cognitive science concepts. Social interaction is basically the result of individual cognition; therefore, the more accurately one can model individual agents and their cognitive processes, the more accurate and dependable the social model will become. The authors also argue that utilizing a realistic cognitive model helps us to better understand individual agent cognition, more specifically the socio-cultural aspects of cognition and how individual agents learn from each another. The paper mentions how the CLARION cognitive architecture is well suited to model cognitively accurate agents within a social simulation. The authors describe CLARION’s dual representation of human learning, and go into detail on how the lower layer (the implicit layer) interacts with the upper layer (the explicit layer). They cite this dual-representation as being fundamental in constructing realistic cognitive agents.

The paper moves on to explain an organizational task that is to be studied. The task is to determine if a blip on a radar screen is a hostile aircraft, a flock of geese, or a civilian aircraft (i.e. – whether the blip is an enemy, neutral, or friendly). Each blip has nine attributes that carry a weighted value. If the sum of the attributes is less than 17 it is friendly, if greater than 19 it is hostile, otherwise it is neutral. The organization is presented with 100 problems (100 blips). An organization’s performance is a measure of how accurately it can classify blips.

Originations are set up in one of two ways, as a team structure or as a hierarchy. In a team structure, every agent’s calculations are equally weighted and the final result is determined democratically. In a hierarchy, agents’ decisions are passed on to a supervisor agent, and the supervisor makes the final decision. Additionally, information is disbursed within the organization in one of two ways. In a distributed fashion where no single agent has access to all required pieces of information, or in a blocked fashion where each agent has access to all pieces of required information. (In both cases, all pieces of information are accessible to the agents as whole.)

Earlier studies, performed by Carley et al in 1998, revealed that humans typically perform better in a team situation, especially when information was provided in a distributed manner (no single agent has access to all pieces of required information). The worst performance occurred in hierarchical team structures that used blocked data access (every agent has access to all pieces of required information). In the same study, Carley used four cognitive architectures, other than CLARION, to simulate the same task. Results from these simulations were compared with human data. The comparison showed that the simulations did not provide results that were inline with human capabilities. It is argued that this is due to shortcomings in the architectures to accurately model human intelligence and learning.

Sun and Naveh simulated the same task with the CLARION cognitive architecture. Results from this simulation were compared with the actual human data and it was found that CLARION was able to provide results that were inline with human results. It is argued that the similarity is due to the fact that CLARION accurately models human learning in its dual representation structure. The authors then augmented the simulation by extending runtime, by varying cognitive parameters (such as learning rate, and temperature of randomness), and by introducing differences in the cognitive agents (such as introducing weak learners). The overall result is that when using CLARION, one can create cognitively realistic social simulations yielding results that are in accord with psychological literature.

A Summary of "Collective Intelligence: Mankind’s Emerging World in Cyberspace"

Citation
Levy, Pierre. "Introduction." Collective Intelligence: Mankind’s Emerging World in Cyberspace. Cambridge, Massachusetts: Perseus Books, 1999. 1-19.

Summary / Assessment
In the introduction to his book, Levy first discusses collective intelligence as it relates to economy. He writes: “the prosperity of a nation, geographical region, business, or individual depends on their ability to navigate the knowledge space.” The more we can form intelligent, highly capable communities, the more we can ensure our success in a highly competitive environment. As an example: businesses transform themselves to promote information exchange between departments. This results into what Levy calls “innovation networks”. Departments can easily interact with one another transferring knowledge, personnel, and skills. This allows companies to be more receptive to ever changing demand for skills (such as scientific, technical, social, and aesthetic skills). An organization that is inflexible to changing skills, will eventually collapse.

Levy defines an anthropological space as: “a system of proximity (space) unique to the world of humanity (anthropological), and thus dependant on human technologies, significations, language, culture conventions, representations, and emotions”. Four spaces are defined: earth, territorial space, commodity space, and knowledge space. The Earth Space defines our identity in terms of our “bond with the cosmos”, as well as our affiliation or alliance with other humans (ex: our name is a symbol representing out place in an ancestral line). In the Territorial Space, the meaning of identity shifts. Identity, in this space, is linked with the ownership of property and group affiliations (ex: our home address identifies our geographic location as well as affiliations with certain groups of individuals (our neighborhood)). In the Commodity Space, identity is defined by one’s participation in the process of moving commodities (goods). This includes involvement in the production of goods and involvement in the exchange of goods. In the fourth space, the Knowledge Space, one’s identity is defined by knowledge and the capacity to rapidly acquire knowledge. Levy identifies three aspects of the Knowledge Space: Speed – information/knowledge can rapidly be acquired. Mass – It is impossible to restrict the movement of knowledge; therefore a larger mass of individuals now has access to information/knowledge. Tools – tools have been created that enable individuals to acquire, manage, and filter information as needed.

Levy defines collective intelligence as “a form of universally distributed intelligence, constantly enhanced, coordinated in real-time, and resulting in the effective mobilization of skills.” Knowledge is enhanced at the level of the individual as we try to better ourselves through the acquisition of new skills and abilities. Intelligence is coordinated in real-time through digital mediums and emergent technologies. Skills are mobilized first by acknowledging an individual’s skills, and then by recognizing the individual’s contributions to the collective.

Levy also adds to his definition of collective intelligence by writing, “the basis and goal of collective intelligence is the mutual recognition and enrichment of individuals, rather than the cult of fetishized or hypostatized communities”. In other words, the goal of collective intelligence should be that individuals are compensated or acknowledged based on the quantity and more importantly, the quality of their contribution to the collective. Furthermore, acknowledgement should be made at the level of the individual, rather than at the level of the collective.