Abstract
In spite of significant research efforts, argument technologies do not seem poised to scale up as much as most commentators would hope or even predict. In this paper, I discuss what obstacles bar the way to more widespread success of argument technologies and venture some suggestions on how to circumvent such difficulties: doing so will require a significant shift in how this research area is typically understood and practiced. I begin by exploring a much broader yet closely related question: To what extent are people natively good at arguing? This issue has always been central to philosophical reflection and it has become even more urgent nowadays, with the explosion of persuasive technologies and unprecedented opportunities for large-scale social influence. The answer hinges on what aspect of argumentation is taken under consideration: evidence suggests that people are relatively bad at analyzing the structure of arguments, especially when these are presented out of context and in abstract terms; in contrast, data show that even laymen tend to excel in the interactive practice of argumentation, in particular when motivation is high and something significant is at stake. Unfortunately, current argument technologies are more closely tailored to the former type of activity than to the latter, which is the main reason behind their relative lack of success with the general public. Changing this state of affair will require a commitment to ecological argument technologies: that is, technologies designed to support real-time, engaging and meaningful argumentative interactions performed by laypeople in their ordinary life, instead of catering to the highly specific needs of a minority of niche users (typically, argumentation scholars).
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Notes
“Argument”, retrieved December 28, 2015, from http://www.merriam-webster.com/dictionary/argument
CMNA, Computational Models of Natural Arguments, since 2001 (http://www.cmna.info/); ArgMAS, Argumentation in Multi-Agent Systems, since 2004 (http://www.mit.edu/~irahwan/argmas/); UM4Motivation, User Models for Motivational Systems, in 2011 and 2012 (http://cgi.csc.liv.ac.uk/~floriana/UM4Motivation2/Home.html)
COMMA, Computational Model of Arguments, since 2006 (http://www.comma-conf.org/)
Just to mention a few: ASPIC, Argumentation Service Platform with Integrated Components (http://cordis.europa.eu/ist/kct/aspic_synopsis.htm); DAM, Dialectical Argumentation Machines (http://www.arg-tech.org/index.php/projects/dialectical-argumentation-machines/); DYNARG, The Dynamics of Argumentation (http://icr.uni.lu/dynarg/DYNARG/Home.html); and the recent EPSRC large grant on Argument Mining (http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/N014871/1)
The EU COST action Agreement Technologies (http://www.agreement-technologies.eu/)
AT, International Conference on Agreement Technologies, since 2012 (http://www.agreement-technologies.eu/resources/at-conference-series)
The notion of ecological argument technologies detailed in this paper refers to the broader concept of ecological rationality, as developed by Gigerenzer and Selten (2001) and Gigerenzer et al. (2011). The ties between the present proposal and this line of work will be further discussed later on: for now, let us just say that an argumentative technology, in order to be ecological in the required sense, ought to be designed to match the argumentative skills human users developed in response to the ecological pressures they face in everyday life.
Non-ecological argument technologies may still be very valuable for dedicated purposes, e.g., education, and may even provide guidance on how to design more productive and sustainable platforms and tools. What they cannot do, however, is to scale up, as long as they fail to meet the ecological challenge. This is the claim articulated in this paper, which of course does not deny the potential usefulness of argument technologies also in more restricted domains.
Both the universality and the incorrigibility claims are tied to the tradition of classifying fallacies at a high level of abstraction, as idealized inference patterns—a tradition criticized by van Eemeren and Grootendorst (1995), among others. In contrast, situated learning theory (Lave and Wenger 1991) has provided evidence that otherwise common errors occur less frequently (and are more varied) in concrete problem-solving situations, as opposed to abstractly defined problems. An echo of this line of work can be found in recent attempts to articulate more nuanced versions of fallacy theory, which will be discussed in Section 2.2 (see also Boudry et al. 2015; Paglieri 2016).
The key documents to consult, in order to get a sense of the dominant institutional view on critical thinking education in Italy, are the current guidelines on the curriculum of Italian kindergartens and primary and secondary schools (“Indicazioni nazionali per il curricolo della scuola dell’infanzia e del primo ciclo di istruzione,” http://www.indicazioninazionali.it/documenti_Indicazioni_nazionali/indicazioni_nazionali_infanzia_primo_ciclo.pdf), issued by the Italian Ministry of Education and Research (MIUR) in September 2012. In that document, critical thinking is identified as a priority across all disciplines, in spite of the utter lack of (i) dedicated critical thinking training for teachers and (ii) resources to support the proclaimed shift towards more rigorous critical thinking education in Italian schools.
Source: http://www.admissionstestingservice.org/images/47832-tsa-test-specification.pdf (last consulted on December 27, 2015)
Source: http://www.admissionstestingservice.org/for-test-takers/thinking-skills-assessment/tsa-cambridge/about-tsa-cambridge/ (last consulted on December 27, 2015)
Source: http://www.merton.ox.ac.uk/admissions-feedback-economics-and-management (last consulted on December 27, 2015)
Given all these differences across groups, in terms of test materials, sample size, methodology, context, etc., running statistical analyses on these data would be rather uninformative. Thus, statistical details will be kept to a minimum and provided only for those comparisons that are methodologically appropriate. For the same reason, these findings are meant here only to illustrate the difficulties that various sorts of people encounter in dealing with abstract argument analysis.
Of course, the fact that we are forced to admit the existence of non-fallacious fallacies is just another indication of the theoretical inadequacy of the standard notion of a fallacy: a category designed to capture erroneous reasoning ends up including in its extension also acceptable forms of inference. But what to do about the problematic status of fallacy theory is a topic for another day (for discussion, see Woods 2013; Paglieri 2016).
These results should not be confused with the claim that people are “good with probabilities,” since there is ample evidence they are not—witness the garden variety of well-known biases of probabilistic reasoning, e.g., gambler’s fallacy (Tversky and Kahneman 1971), conjunction fallacy (Tversky and Kahneman 1983), and base rate neglect (Tversky and Kahneman 1982). What these findings show, instead, is that people track the quality of argument based on the same factors that would be relevant if Bayesian update was used: the observed similarity is at the level of the outcome, with no claim being made on a corresponding similarity of mechanisms—indeed, people are certainly not doing Bayesian computation as part of their explicit reasoning. The fact that we get the outcome right when evaluating each other’s arguments, while we do not in solitary reasoning tasks of the sort used by Tversky and Kahneman, provides further support to the argumentative theory of reasoning.
Similar considerations apply also to (and have received much more attention in) instructional design in computer-supported collaborative learning, as documented by scripting studies; for a discussion of several applications to argument education, see Weinberger et al. (2007).
While this may seem reminiscent of Facebook likes, it is actually not—or, more precisely, it aims to achieve the same level of intuitive appeal but for entirely different purposes. What prevents Facebook likes from being used as argumentative indicators is their ambiguity of meaning: by liking a post, a comment, a photo, or anything else, a Facebook user may express a variety of communicative intentions—approval of the contents, support, or solidarity for the author of the contents, approval for the fact that the author of the post decided to make it public, hilarity prompted by the contents or their posting, and more. Argument technologies should strive to provide users with tagging options that are as appealing as Facebook likes but with a much better defined semantics.
Interestingly, expecting users to dwell too much on argument structure may be not only unrealistic but even undesirable. A recent study on using Facebook for learning purposes (Tsovaltzi et al. 2015b) showed that participants who had carefully prepared their own arguments on a given topic were less likely to interact productively with other SNS users on that topic, compared to people without that sort of individual preparation—yet another finding fully compatible with the argumentative theory of reasoning. That led the authors to conclude that “directly interacting with the support of argumentation scripts and without long individual preparation and reflection may be preferable to carefully preparing arguments before joining discussion in a SNS” (Tsovaltzi et al. 2015b, p. 588).
Crucially, complex argument annotation systems are problematic also for experts, not only for laypeople. When using a simplified version of Walton’s argumentation scheme taxonomy (including 14 schemes out of 60), with coders trained on argumentation and four iterations of the annotation procedure, the final level of inter-coder agreement can still be as low as 0.48, measured by Cohen’s kappa (Schneider et al. 2013). This figure raises to more acceptable levels only when simpler annotation schemes are adopted (Schneider et al. 2012b), and it is further improved by isolating ex post the most reliable sub-groups of annotators (Peldszus and Stede 2013b).
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Paglieri, F. A Plea for Ecological Argument Technologies. Philos. Technol. 30, 209–238 (2017). https://doi.org/10.1007/s13347-016-0222-6
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DOI: https://doi.org/10.1007/s13347-016-0222-6