Resolving Disagreement Through Mutual Respect

Martini, Carlo; Sprenger, Jan; Colyvan, Mark

doi:10.1007/s10670-012-9381-8

Cited by 13 publications

(6 citation statements)

References 29 publications

(26 reference statements)

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“…One final worry might be that this interpretation of iterated pooling appears incompatible with the standard one, the one compatible with the classical "peer disagreement problem" (Hartmann et al 2009;Martini et al 2013), in which differences of opinion persist even when there is no difference in private information. In such contexts iterated pooling has been suggested as rational arbitration mechanism.…”

Section: Discussionmentioning

confidence: 99%

All agreed: Aumann meets DeGroot

Romeijn

Roy

2017

Theory Decis

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Section: Discussionmentioning

confidence: 99%

All agreed: Aumann meets DeGroot

Romeijn

Roy

2017

Theory Decis

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“…This is not the only possible interpretation, however. One can instead interpret the bias parameter as a form either of comparative expertise, much in the spirit of (Estlund 1997), or of "mutual respect" (Martini, Sprenger, and Colyvan 2013). In the first case, the parameter represents how much more of an "expert" on the question at hand each participant sees herself as being in comparison to the others.…”

Section: Normative Interpretation Of the Modelmentioning

confidence: 99%

Deliberation, Single-Peakedness, and Coherent Aggregation

Rad

Roy

2021

Am Polit Sci Rev

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Rational deliberation helps to avoid cyclic or intransitive group preferences by fostering meta-agreements, which in turn ensures single-peaked profiles. This is the received view, but this paper argues that it should be qualified. On one hand we provide evidence from computational simulations that rational deliberation tends to increase proximity to so-called single-plateaued preferences. This evidence is important to the extent that, as we argue, the idea that rational deliberation fosters the creation of meta-agreement and, in turn, single-peaked profiles does not carry over to single-plateaued ones, and the latter but not the former makes coherent aggregation possible when the participants are allowed to express indifference between options. On the other hand, however, our computational results show, against the received view, that when the participants are strongly biased towards their own opinions, rational deliberation tends to create irrational group preferences, instead of eliminating them. These results are independent of whether the participants reach meta-agreements in the process, and as such they highlight the importance of rational preference change and biases towards one’s own opinion in understanding the effects of rational deliberation.

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“…Scientific disagreement, i.e., "situations in which scientists hold different opinions or theories, or in which they hold different views about scientific results, or even about the processes to obtain such results" (Parkkinen et al, 2017, p. 76), has fallen within the research interests of philosophers (Matheson & Frances 2018;Martini et al, 2013) for some time. Still, the situations when econometricians obtain divergent or even inconsistent causal hypotheses from similar (or even the same) datasets are poorly understood and raise heated methodological debates.…”

Section: The Malleability Of Econometric Modelingmentioning

confidence: 99%

Resolving empirical controversies with mechanistic evidence

Maziarz

2021

Synthese

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The results of econometric modeling are fragile in the sense that minor changes in estimation techniques or sample can lead to statistical models that support inconsistent causal hypotheses. The fragility of econometric results undermines making conclusive inferences from the empirical literature. I argue that the program of evidential pluralism, which originated in the context of medicine and encapsulates to the normative reading of the Russo-Williamson Thesis that causal claims need the support of both difference-making and mechanistic evidence, offers a ground for resolving empirical disagreements. I analyze a recent econometric controversy regarding the tax elasticity of cigarette consumption and smoking intensity. Both studies apply plausible estimation techniques but report inconsistent results. I show that mechanistic evidence allows for discriminating econometric models representing genuine causal relations from accidental dependencies in data. Furthermore, I discuss the differences between biological and social mechanisms and mechanistic evidence across the disciplines. I show that economists mainly rely on mathematical models to represent possible mechanisms (i.e., mechanisms that could produce a phenomenon of interest). Still, claiming the actuality of the represented mechanisms requires establishing that crucial assumptions of these models are descriptively adequate. I exemplify my approach to assessing the quality of mechanistic evidence in economics with an analysis of two models of rational addiction.

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Resolving Disagreement Through Mutual Respect

Cited by 13 publications

References 29 publications

All agreed: Aumann meets DeGroot

All agreed: Aumann meets DeGroot

Deliberation, Single-Peakedness, and Coherent Aggregation

Resolving empirical controversies with mechanistic evidence

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