Intelligent agents as a modelling paradigm
Bibliographic record
Abstract
Intelligent software agents have been used in many applications because they provide useful integrated features that are not available in "traditional" types of software (e.g., abilities to sense the environment, reason, and interact with other agents). Although the usefulness of agents is in having such capabilities, methods and tools for developing them have focused on practical physical representation rather than accurate conceptualizations of these functions. Like other computer systems, intelligent agents usually represent some real world phenomena or environments. Consequently, intelligent agents should closely mimic aspects of the environment in which they operate. In the physical sciences, a conceptual model of a problem can lead to better theories and explanations about the area. Therefore, we ask how can an intelligent agent conceptual framework, properly defined, be used to model complex interactions in various social science disciplines? The constructs used in the implementation of intelligent agents may not be appropriate at the conceptual level, as they refer to software concepts rather than to application domain concepts. Therefore we propose to use a combination of the systems approach and Bunge's ontology as adapted to information systems, to guide us in defining intelligent agent concepts. The systems approach will be used to define the components of the intelligent agents. Once the components have been identified we will use ontology to understand the configurations, transitions, and interrelationships between the components. We will then provide a graphical representation of these concepts for modelling purposes. As a proof of concept for the proposed conceptual model, we apply it to a marketing problem and implement it in an agent-based programming environment called Netlogo. With the aid of the conceptual model, the user was able to quickly visualize the complex interactions of different agents. The use of the conceptual representation even sparked an investigation of previously neglected causal factors which led to a better understanding of the problem. The implications of these findings, and further research avenues, are also discussed. Since the proof of concept was successful, it can be said that we provide an intelligent agent framework that can graphically model phenomena in the social sciences. However, there are other contributions derived from the work, including; a theoretically driven concept of intelligent agent components, a way of showing the interrelationships between these concepts, and the foundation for an ontologically complete model of intelligent agents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".