Abstract
In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. The model is an improved variant of the ABM introduced in [3], which is based on abstract argumentation frameworks. The current model employs a more refined notion of social networks and a more realistic representation of knowledge acquisition than the previous variant. Moreover, it includes two criteria of success: a monist and a pluralist one, reflecting different desiderata of scientific inquiry. Our findings suggest that, given a reasonable ratio between research time and time spent on communication, increasing the degree of connectedness of the social network tends to improve the efficiency of scientists.
The research by AnneMarie Borg and Christian Straßer is supported by a Sofja Kovalevskaja award of the Alexander von Humboldt Foundation and by the German Ministry for Education and Research.
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Notes
- 1.
Accordingly, we can distinguish between models that provide how actually and how possibly explanations. While some have suggested that modeling a possibility is epistemically valuable [11], others have argued that if a model merely captures a possibility, it is epistemically and pragmatically idle [19]; instead, the presented possibility has to be understood within a specified context, which makes it relevant for real world phenomena.
- 2.
To this end, one of the tasks for future research is the empirical calibration of the parameters used in the model, as pointed out in [13].
- 3.
Our ABM is created in NetLogo [22]. The source code is available at: https://github.com/g4v4g4i/ArgABM/tree/LORI-VI-2017.
- 4.
Other ways of discovering arguments and attacks (via social networks) are discussed below.
- 5.
Given that theories in our model are conflict-free, our notion of admissibility is actually the same as the one introduced in [7]. In Dung’s terminology, our sets of defended arguments correspond to preferred extension (which are exactly the maximally admissible sets), except that we determine these sets relative to given theories.
- 6.
Such hunches are not considered when agents evaluate theories.
- 7.
In contrast to the current model, in AABMSI [3] network structures are generated probabilistically in specific time intervals.
- 8.
A representative agent is excluded from research for 1–4 rounds: she always pays the basic cost of information sharing which is 1 round, and in addition, for every 2 fully explored arguments she will pay an additional round. The cost of learning an attack is equivalent to learning one degree of exploration of an argument.
- 9.
Further parameters, with short explanations, are as follows. The move probability (set to 0.5) together with the degree of exploration of the argument an agent is situated at, determines the chance that she will move to another argument every 5 rounds (the move incentive is further decreased by \(\frac{1}{5}\) for time steps in between). The visibility probability (set to 0.5) is the probability with which a new attack is discovered when an agent further explores her argument. The research speed (set to 5) determines the number of time steps an agent has to work on an argument a before a reaches its next level of exploration. The strategy threshold (set to 0.9) concerns the fact that each theory with a degree of defensibility that is at least 90% of the degree of defensibility of the best theory is considered good enough to be researched by agents. The jump threshold (set to 10) concerns the number of evaluations an agent can remain on a theory that is not one of the subjectively best ones.
- 10.
While our criterion is moderately pluralist, a more radical version would make plurality a necessary condition of success (i.e. populations would be punished for converging on one theory). We leave this consideration for future research.
- 11.
The plots concern the landscape consisting of three theories. The results were similar in case of two theories in all the discussed respects, except that the agents were comparatively more efficient.
- 12.
One example of an ABM that studies deception in science is [12], which examines the effects of a deceptive agent in a community of epistemically pure agents. The authors show that in general a higher degree of connectivity helps against deceptive information. While our model doesn’t examine the case of mixed (reliable and deceiving) agents, our results are, generally speaking, in line with their results.
- 13.
Interestingly, comparing the results of our model that employs the heuristic behavior (HB) and the results produced when HB is removed, shows that HB has hardly any impact on the success of agents, and in some cases it even slightly lowers their success. This seems to suggest that HB, by making agents stay on an undefended argument, waiting to find how to defend it, shields not only the best theory but also the worse ones, leading to an overall less successful inquiry. Examining this issue in more detail remains a task for future research.
- 14.
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Borg, A., Frey, D., Šešelja, D., Straßer, C. (2017). Examining Network Effects in an Argumentative Agent-Based Model of Scientific Inquiry. In: Baltag, A., Seligman, J., Yamada, T. (eds) Logic, Rationality, and Interaction. LORI 2017. Lecture Notes in Computer Science(), vol 10455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55665-8_27
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