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How Should We Reason? Philosophical and Psychological Perspectives (October 12-13 2023)

Idea & Motivation

According to a long-standing insight going back to Hume, the normative and the descriptive are distinct in kind, so that it is wrong to conclude “is” from “ought” or vice versa. And yet, in a variety of contexts, the normative and the descriptive seem to be closely intertwined in many ways. Accordingly, the relationship between the two is as yet poorly understood. This is detrimental not only to philosophical efforts at normativity, but also to descriptive research that draws on normative concepts. This workshop will comprehensively discuss this intertwining. In particular, we are interested in exploring different theoretical frameworks for theory building and examining case studies associated with new normative challenges. In doing so, the workshop will not least promote interdisciplinary dialogue on human reasoning and argumentation. The workshop is part of the AHRC-DFG funded project “Normative vs. Descriptive Accounts in the Philosophy and Psychology of Reasoning and Argumentation: Tension or Productive Interplay? ”.

Invited Speakers


A two-day workshop. Invited speakers speak 75 min, contributed speakers 30 min. We allow for plenty of time for informal discussions.


To register, please send a message to Borut Trpin borut.trpin AT by September 10, 2023.


LMU München

Geschwister-Scholl-Platz 1

ZEPP - Room M 210

80539 München

Day 1 (12 October 2023)

08:45 - 09:00 Registration
09:00 - 09:15 Welcome Address
09:15 - 10:30 Finnur Dellsén: "Inferring to the Best Explanation from Uncertain Evidence"
10:30 - 10:45 Coffee Break
10:45 - 12:15 Contributed Talks: Vincenzo Crupi and Fabrizio Calzavarini: "Norms vs. evidence in reasoning research"
Corina Strößner and Ulrike Hahn: "Reasoning: Bridges across the normative-descriptive divide"
Lars Schärer: "Routes of Transition: from Descriptive to Normative in Medicine"
12:15 - 13:30 Lunch Break
13:30 - 14:45 Magdalena Małecka: "Revisiting normative theory of decision-making in economics"
14:45 - 15:00 Coffee Break
15:00 - 16:30 Contributed Talks:
Alex Gebharter and Barbara Osimani: "A Bayesian approach to evaluation policy efficacy across domains"
Matteo De Benedetto and Matias Osta-Velez: "The Normative Dimensions of Logical Modelling"
Stephan Hartmann and Borut Trpin: "Coherentism, Explanationism, and Explanatory Power"
16:30 - 17:00 Coffee Break
17:00 - 18:15 Nevin Climenhaga: "A Unified Theory of Probability"
19:00 Workshop Dinner

Day 2 (13 October 2023)

09:15 - 10:30 Katya Tentori: "How should we measure forecast accuracy? Combining Formal Models and Human Intuition for new Cutting-Edge Scoring Rules"
10:30 - 11:00 Coffee Break
11:00 - 12:30 Contributed Talks:
Nicole Cruz: "Comparing Measures of Inference Strength"
Martina Calderisi: "On the reality of the base-rate fallacy"
Edoardo Rivello: "Reasoning about truth: Gupta’s puzzle as a case study"
12:30 - 13:45 Lunch Break
13:45 - 15:00 Stefanie Egidy's keynote talk
15:00 - 15:30 Coffee Break
15:30 - 16:30 Contributed Talks:
James Grayot: "Why be a frame-sensitive reasoner?"
Nastja Tomat: "Bounded Epistemic Rationality as a Link between the Normative and the Descriptive"
16:30 - 17:00 Coffee Break
17:00 - 18:15 Wolfgang Spohn: "Descriptive and Normative Reasoning, Reason in a Normative and a Descriptive Perspective."


Nevin Climenhaga: A Unified Theory of Probability

I defend a partial entailment/degree-of-support interpretation of probability as a unified theory of both physical and epistemic probability. On this interpretation, probabilities are necessary and a priori relations between propositions, and measure the degree to which one proposition supports, or "partially entails", another. I begin by providing a fuller explication of the structure of support relations than previous defenders of this interpretation. I distinguish between basic probabilities, which are determined directly by the content of the propositions they relate, and non-basic probabilities, which are determined directly by the values of other probabilities, and only indirectly by the content of the propositions they relate. I argue that basic probabilities are the probabilities of effects conditional on direct influences. After this, I explain what both physical and epistemic probabilities are, on this theory; note advantages this theory has over rival theories; and consider the question of what determines the values of basic probabilities, with a focus on the prospects of the principle of

Katya Tentori: How should we measure forecast accuracy? Combining Formal Models and Human Intuition for new Cutting-Edge Scoring Rules

While anticipating future events is fundamental to human cognition, there is no consensus on how to measure forecast quality. We introduce a novel experimental method for eliciting participants’ ordinal accuracy judgments and compare them with the expectations set by Quadratic, Logarithmic, and Spherical scoring rules. The results reveal that the Logarithmic rule generally aligns better with participants’ judgments, although some systematic deviations exist. To address this, we propose a new measure, 'BLog' (Borda-adjusted-Log score), which combines the Logarithmic rule with the Borda count's ranking algorithm. We demonstrate that 'BLog' is not only a strictly proper measure but also outperforms existing

Wolfgang Spohn: Descriptive and Normative Reasoning, Reason in a Normative and a Descriptive Perspective

First, I address reasoning in general. Usually, this is understood as, or restricted to, descriptive reasoning, or reasoning about truth. Secondly, I shall ask how to extend accounts of reasoning to normative reasoning, which is widely understood not to be about truth. Thirdly, the most pressing issue is the interaction of descriptive and normative reasoning. This interaction has been taken as a logical impossibility, but its existence can’t be denied, though it is hard to account for it. I will indicate a proposal. After this, I will, fourthly, discuss how the normative/descriptive distinction applies to the theory of rationality itself. And this reflection will, fifthly and finally, lead us to what I take to be a fundamental principle guiding the interaction between normative and descriptive

Finnur Dellsén: Inferring to the Best Explanation from Uncertain Evidence

This paper presents a new problem for Inference to the Best Explanation (IBE), conceived of either as a fundamental form of inference or as a heuristic for approximating Bayesian reasoning. In short, I suggest that IBE faces a problem concerning uncertain evidence that is structurally similar to the problem that motivates many to generalize Bayesian Conditionalization to Jeffrey Conditionalization. In many cases, I suggest, uncertainty about parts of one’s evidence E undermines the inferrability of a given hypothesis H that would provide the best explanation of E, for there may be another hypothesis H' that would provide a better explanation than H if the uncertain pieces of evidence were not included in E. However, no current formulation of IBE as a fundamental or heuristic inference rule is even capable of representing such uncertainty about the evidence -- let alone accommodate the fact that this may undermine the inferrability of H -- for they either count a given piece of uncertain evidence as entirely included in, or entirely excluded from, E. As a result, IBE cannot currently accommodate cases in which scientists rightly hesitate to infer the best explanatory hypothesis on the grounds that they are uncertain about some of the evidence to be explained. Motivated by recent and historical instances of explanatory reasoning in science, I suggest that a promising approach to the problem invokes a notion of 'evidential robustness'. Roughly, an inference from E to H is evidentially robust just in case the same inference rule would have licensed inferring H from a somewhat different set of evidence E'.top

Practical Information

How to get to the venue by public transport
Train: Arrival at München Hauptbahnhof (Munich Main Station) or München Ostbahnhof (Munich East Station), then take the S-Bahn to Marienplatz and from there the U3 or U6 to stop Universität. To plan your travel, visit .

S-Bahn (City train): All lines to Marienplatz, then U-Bahn.

U-Bahn (Metro): Line U3/U6, stop Universität.

Bus: Line 53, stop Universität.

Arrival by plane:
From Airport Munich take the S-Bahn S1 or S8 to Marienplatz and then proceed with the U-Bahn U3/U6 to stop Universität (University). By taxi the distance from the airport to the university is around 30km.



The workshop is part of the AHRC-DFG funded project “Normative vs. Descriptive Accounts in the Philosophy and Psychology of Reasoning and Argumentation: Tension or Productive Interplay? ”.