Abstract
Recent studies of scientific interaction based on agent-based models (ABMs) suggest that a crucial factor conducive to efficient inquiry is what Zollman (2010) has dubbed ‘transient diversity’. It signifies a process in which a community engages in parallel exploration of rivaling theories lasting sufficiently long for the community to identify the best theory and to converge on it. But what exactly generates transient diversity? And is transient diversity a decisive factor when it comes to the efficiency of inquiry? In this paper we examine the impact of different conditions on the efficiency of inquiry, as well as the relation between diversity and efficiency. This includes certain diversity-generating mechanisms previously proposed in the literature (such as different social networks and cautious decision-making), as well as some factors that have so far been neglected (such as evaluations underlying theory-choice performed by scientists). This study is obtained via an argumentation-based ABM (Borg et al. 2017, 2018). Our results suggest that cautious decision-making does not always have a significant impact on the efficiency of inquiry while different evaluations underlying theory-choice and different social networks do. Moreover, we find a correlation between diversity and a successful performance of agents only under specific conditions, which indicates that transient diversity is sometimes not the primary factor responsible for efficiency. Altogether, when comparing our results to those obtained by structurally different ABMs based on Zollman’s work, the impact of specific factors on efficiency of inquiry, as well as the role of transient diversity in achieving efficiency, appear to be highly dependent on the underlying model.
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Notes
Alexander (2013) presents a slightly different scenario, where the number of rivaling theories grows over time. His results suggest that some learning strategies (namely, the combination of reinforcement and social learning via preferential attachment) can lead to the optimal level of diversity, under the condition that agents discount the knowledge of past theories.
We have omitted a class of models employing epistemic landscapes (such as Weisberg and Muldoon 2009; Alexander et al. 2015; Thoma 2015; Pöyhönen 2017) since they tend to represent a different kind of diversity than the one we are focusing on in this paper: they rather examine what would better be labeled as ‘cognitive diversity’ (Pöyhönen and Kuorikoski 2016), which concerns different research heuristics employed by individual agents across the given community. Moreover, efficiency of inquiry in these models is usually measured in terms of success of the community in discovering certain patches of the given landscape, rather than in terms of agents converging on a single theory.
The model is programmed in NetLogo (Wilensky 1999). The code of the model employed in this paper can be found at: https://github.com/g4v4g4i/ArgABM.
In Borg et al. (2017, 2018) the efficiency in terms of time is measured in a slightly different way. Moreover, in Borg et al. (2017) we present an alternative, ‘pluralist’ measure of success, according to which agents are successful if at the end of the run the best theory isn’t less populated than any of its rivals. In the current section we will try to keep technical details at the minimum. An interested reader can take a closer look at the above mentioned publications on this model.
For the sake of simplicity, we use the terms ‘theory’ and ‘research program’ interchangeably in this paper.
For a concrete example of a scientific controversy—namely, the continental drift debate—represented by means of a similar framework (based on abstract argumentation) see Šešelja and Straßer (2013).
While we can imagine a situation in which a single argument serves as an objection attacking the rivaling theory in whole (for example, showing the theory cannot explain a certain set of phenomena) in the current model we abstract away from such cases by employing the idealization that attacks always target a specific part of a theory (e.g. an attack on a study in a rivaling research program pointing to a methodological problem doesn’t necessarily attack results of other studies within the same program—i.e. other arguments). Note that this is already a step further in the direction of representational adequacy in comparison to Zollman-inspired ABMs. It remains a task for future research to examine whether our results remain robust if we implemented a more detailed representation of argumentative attacks, e.g. by introducing an explanatory relation between arguments and a set of explananda (as it is done by Šešelja and Straßer 2013) and more refined evaluation procedures (as compared to the ones to be introduced in Section 3).
The representation of our landscape is inspired by abstract argumentation frameworks (Dung 1995). Formally, the landscape is given by a triple 〈,⇝,↪〉 where ↪ is the discovery relation, \(\rightsquigarrow \) is the attack relation, and = 〈1,…,m〉 is partitioned in m many theories Ti = 〈i,ai,↪〉 which are trees with ai ∈i as a root and
$${\rightsquigarrow}\subseteq\underset{i \neq j}{\bigcup\limits_{1 \le i, j \le m}}(\mathcal{A}_{i} \times \mathcal{A}_{j}) \quad \text{ and } \quad {\hookrightarrow} \subseteq \bigcup\limits_{1 \le i \le m} (\mathcal{A}_{i} \times \mathcal{A}_{i}). $$Specifying ⇝ like this ensures that the theories are conflict-free, i.e. that there are no attacks between the arguments of the same theory.
Agents discover attacks to and from their current arguments, as well as the child arguments of their current arguments gradually, depending on the degree of exploration assigned to the current argument at a given time point of a run: for each agent ag and each argument
where 0 indicates that the argument is unknown to ag and 6 indicates that the argument is fully explored and cannot be further explored. Since the model is round-based, each round may be interpreted as one research day. Each of the 6 levels of an argument takes a researcher 5 rounds/days of exploration. Thus, each argument represents a hypothesis that needs altogether 30 research days to be fully investigated.The other theories are modeled as having a certain percentage of their arguments attacked and undefended.
In the current model we assume that agents reliably share information, i.e. that they share both positive and negative findings about their current theory. Borg et al. (2018) examine in addition deceptive information sharing, i.e. agents who share only positive findings about their theory (arguments and attacks to other theories), while withholding the information about attacks on their own theory. Whether the results presented in this paper also hold for deceptive agents remains a question for future research.
An alternative way to interpret this assessment is in terms of an explanatory scope of a theory, where we are assuming that the arguments constituting the given theory are explanatory in nature (see Šešelja and Straßer 2013). A less idealized measure of explanatory power could be implemented by introducing a set of explananda E and an explanatory relation from some of the arguments in the theory to a subset of E.
Given that theories in the model are conflict-free, the notion of admissibility is here the same as the one introduced in Dung (1995). 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.
It is easy to show that the following measure results in an equivalent preference order: T1 is preferred to T2 iff |Def(T1)|/|Disc(T1)| > |Def(T2)|/|Disc(T2)|, where Def(Ti) stands for defended arguments.
Rational inertia shouldn’t be confused though with the ‘Steadfast Norm’ discussed by Kelp and Douven in the same paper, and well-known in the literature on peer disagreement. Unlike in their account, in our model we may interpret a scientist as having a rational inertia towards her theory, while having lowered her confidence that the theory is actually true.
Due to space restructions, many of the plots are omitted from the paper and can be found in the Online Appendix of this paper.
For the exact procedure of how the attacks are generated, and the degree of defensibility of the two suboptimal theories such a procedure results in, see Borg et al. (2018).
The reason why we stop the simulation at this point is that otherwise some agents would become ‘idle’: since they have explored their preferred theory fully, the only way they would change their preference is by waiting for other agents to send them new information. Borg et al. (2019) propose an alternative model in which the simulation continues after this point, eventually bringing all agents on the best theory, so that the efficiency is measured in terms of time only (similarly to the ABM proposed by Frey and Šešelja2018a).
In view of an interpretation suggested by Borg et al. (2017), according to which a round stands for a working day, this threshold means that scientists have to wait 10 weeks before being able to change their theory. Of course, different interpretations of the time in the model are possible.
Though we haven’t examined the situation in which agents share a random subset of their knowledge of the landscape (rather than only recently acquired information), the fully connected community would most likely still outperform the less connected networks since, on the one hand, it would still have a less patchier knowledge of the landscape than the other two networks, while on the other hand, such a change is not likely to increase the chance that the community prematurely abandons the best theory.
For example, different hypothesis in medicine concerning the main causes behind a given disease may require knowledge in different medical disciplines. For a further discussion on the importance of including costs of this kind into ABMs of science see Muldoon (2017).
While maverics and followers stand for more or less epistemically risk-averse agents, omnivorse are agents that prioritize independent evidence for their hypotheses, i.e. evidence that is supported by background theories that overlap as little as possible. Obligates, on the other hand, seek sharp evidence that “speaks clearly and firmly” (p.5): the sharper the evidence the more it allows us to increase our credence in a given hypothesis.
Such an approach could capture, for instance, Laudan’s (1977) suggestion that “it is always rational to pursue any research tradition which has a higher rate of progress than its rivals (even if the former has a lower problem-solving effectiveness)” (p. 111, italics in original).
where 0 indicates that the argument is unknown to References
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Acknowledgments
We are grateful to two anonymous reviewers for valuable comments on the previous draft of this paper.
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.
The research of Dunja Šešelja is supported by the DFG (Research Grant HA 3000/9-1).
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Borg, A., Frey, D., Šešelja, D. et al. Theory-choice, transient diversity and the efficiency of scientific inquiry. Euro Jnl Phil Sci 9, 26 (2019). https://doi.org/10.1007/s13194-019-0249-5
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DOI: https://doi.org/10.1007/s13194-019-0249-5