ASC: Concepts and Arguments

The evaluation of the correctness of arguments is the core of this blog post.

We will focus on justifications as premises are to be evaluated with the scientific method. However, the quality of premises must be considered. Only true premises can guarantee the truth of the conclusion, so the reasons must be impeccable. Therefore, acceptable premises can provide for the acceptance of the conclusion. Additionally, all premises must be consistent to form the conclusion.

Deductive inference (validity) is then used to come to the conclusion. An inference is valid, if all premises are true and the conclusion must be true (where the must refers to the relation between premisses and conclusion not the conclusion itself). Consequently, a valid inference cannot have a false conclusion from true premises.

A central feature of valid premises is that if the conclusion is false, then one of the premises must be false. However, if all premises are true, but the conclusion is still false, the inference must be invalid.

Formal validity is based on the structure of the assertion, not the meaning. [latex](X \in  A \lor X \in B) \land \neg X \in B \implies X \in A[/latex].

Material validity is based on the relation between the concepts. E.g. a square has four sides of equal length.

Conditional Claims

A sufficient condition A for B [latex]A\implies B[/latex]. Logically, B must be true if A occurs, but could be true due to different condition C. B is a necessary condition for A [latex]\neg B\implies \neg A[/latex]. A is true at most if B is true, however, there could be a C that is also necessary for A to be true.

Inferential schemes for conditional claims If [latex]A[/latex] then [latex]B[/latex] are Modus Ponens [latex]A\implies B[/latex] and Modus Tollens [latex]\neg B \implies \neg A[/latex]. Invalid schemes include denying the antecedent [latex]\net A\implies \neg B[/latex] and affirming the consequent [latex]B\implies A[/latex] are a formal fallacy in reasoning (non-sequitur).

In the fallacy of equivocation the same expression is used in different ways in the premises than in the conclusion.

In the naturalistic fallacy a normative claim is deduced from a descriptive claim.

Non-deductive inferences claim to be correct (but not valid). An inference is correct iff its premises together provide a good reason for accepting its conclusion. However, a central characteristics of correct non-deductive inferences is that the conclusion can be false, even if the premises are true. The conclusion is supported with different degrees and can be strengthened or weakened with additional premises. Non-formal fallacies may occur if the reasons are too weak to support the conclusion.

Inductive inferences are an important class of non-deductive inferences, where the premises are analysed with the help the theory of probability and statistics. Enumerative induction concludes from a sample property distribution the whole population property distribution. Statistical syllogism derives from a population that two properties have been observed in common and concludes that one implies the other. Predictive induction observes two properties in a sample and concludes that one implies the other. Usual fallacies include too small samples, non-representative samples, relevant information not considered, and false deliberation regarding probability.

Argument by analogy

A claim is justified by analogy to another claim. This argument is often fallacious, as illustrative analogies (do not justify conclusions), irrelevant analogies, weak analogy, and not considering a relevant disanalogy.

Causal inferences

A factor F is considered to be a causally relevent if for an event, two situations must differ in that the event only occurs in the situation in which F is present. Typicall fallacies include inference from temporal sequence, inference from positive correlation, and inference the inverse causal relevance.

Inference to Best Explanation

A hypothesis is justified because it is the best (closest) explanation for the obtaining of certain facts.

Rules of reasoning

Shifting the burden of proof, instead of justifying a controversial claim, is done by attacking the opponents position or demanding justification. Other ways of shifting are appeals to authority.

The relevance of reasoning demands that an argument is in favour of owns claim. Throwing in arguments that are not related to the claim break relevance.

The accuracy of reasoning is undermined by a “straw man”-fallacy, where an exaggeration or change of claims of the opponent is introduced to make it more susceptible to criticism. More generally, a different claim is attributed to opponent to attack them.

The freedom of speech needs to be preserved by allowing criticism and justification of arguments. Fallacies include argument ad baculum (if you believe A than you believe B), argument ad misercordiam (have pity because X), and argument ad hominem (attacking the person, rather than the argument).

Implicit premises must be stated if they complete the argument. Fallacies include attributing false implicit premises to opponents and not accepting implicit premises in one owns arguments.

Shared premises have to be accepted to reach a reasonable agreement about a controversial claim. Fallacies include retreating from shared premises or attributing claims to be shared premises.

Accepting results of previous argumentation is necessary. Otherwise fallacies such as argument ad ignorantiam  (taking absence of evidence as evidence of absence) or equating the defence of a claim with its acceptance.


ASC: Introduction

 Argumentation and Science Communication will discuss how scientific arguments are made and how they are eventually communicated.

The first weeks readings are listed in the references (Bradley & Steele, 2015; Lempert, Nakicenovic, Sarewitz, & Schlesinger, 2004). A particular focus will be on (Mueller, 2010), for which the following questions should be answered:

  1. What is a computer model?
  2. What are the basic reasons for limits/errors/uncertainties of (climate) models or model predictions? Which ones, do you think, are most important, and for which situations?
  3. What kind of results are generated by models?
  4. What are reasons for and against the claim that computer models inform us about the world?

Scientific knowledge makes sense in its context, but communicating it across fields or indeed beyond science makes it necessary to pick an appropriate language. There is another issue: science is asked to be informative rather than prescriptive, however, usually it is presented/perceived in a prescriptive manner.


Argumentation is needed in policy analysis because decisions are made under deep uncertainty. Argumentation analysis is a philosophical method to address the uncertainty.

Predict-then-act (Lempert, Nakicenovic, Sarewitz, & Schlesinger, 2004)

A rational decision for policy alternatives which are ranked on the basis of their expected utility, contingent on the probabilities of alternative future states of the world. Under Rational Choice Theory the outcome with the highest utility would be picked. Rational Choice would therefore require no democratic deliberation, if science is done properly. However, each step of the scientific endeavour requires deliberation. Science itself is agenda-setting and therefore cannot be performed unquestioned. The rational choice assumption are not fulfilled.

 [Deep uncertainty exists when] decision-makers do not know or cannot agree on: (i) the system models, (ii) the prior probability distributions for inputs to the system model(s) an their inter-dependencies, and/or (iii) the value system(s) used to rank alternatives. (Lempert et al., 2004)

Such a reductive approach is an idealisation that abstracts many aspects. On the upside, it is a smart formal approach that can represent  diverse measures within one holistic measure (expected utility). On the downside, it may not matter for a decision (different statistical value of life for different countries) or it does not apply (requirements may not e fulfilled due to a lack of knowledge beyond information about outcomes).

Summa summarum, rational choice should be seen as a special case, rather than a general paradigm to conceive a policy decision problem. Argumentation is necessary to delineate the problem and frame the options, characterise uncertainties of outcomes, characterise value uncertainties, evaluate uncertainties, and deliberate a fair decision from plural perspectives (Hansson & Hirsch Hadorn, 2016).


Philosophical methods address two questions in a systematic way:

  1. What do you mean? Answer requires an analysis of respective concepts.
  2. How do you know? Answer requires an analysis of respective arguments.

Philosophy does not generate new knowledge, but refers to what is known. It makes a relevant distinction and describes consequences for normative claims about knowledge (epistemology) and action (ethics). It points at the limits of what we can belief and therefore can be considered a method for critical thinking.

Critical Thinking

The goal is to argue, communicate and act responsibly. It requires the ability to accept criticism as well as a willingness to reach an understanding. Based on expertise in a scientific field or regarding real-world problems, critical thinking tries to apply cognitive skills to answer the two basic questions of philosophy mentioned above.

Short Guide to Analysing Texts (Brun & Hirsch Hadorn, 2014)

A guide that structures analysing texts in 5 steps:

  1. Rules for Working and Principles of Understanding
    • Refer explicitly to the analysed text.
    • Write down results and your main reasoning.
    • Develop text analyses and results which are understandable to others.
    • Give reasons for your text analyses/results.
    • Read texts several times to test and revise understanding of both the parts and the whole.
    • Start with the assumption that the author makes true statements and gives sound arguments and go on to test this assumption (principle of charity).
  2. Preparing the Text Analysis: How to Proceed
    • Gather basic information about the text: Authors (ghostwriting?), publishing (scientific or popular?), topic, context of writing and publishing, target readership, type/function of text, and impact
  3. Reading: How to Work on the Text
  4. Structuring: How to Analyse the Structure of the Text
    • Divide the text into smaller passages (e.g. paragraphs) and number them consecutively
    • For every passage of text, consider the following questions:
      1. Content: What is this passage about?
      2. Function: Which function does this passage serve?
      3. Summary: How would you title the passage (content- and function-wise)?
  5. Summarising: How to Capture the Essential
    • Represent concisely essential statements, central arguments and the basic structure of the text.
    • Summaries have to be comprehensible without acquaintance with the original text.
    • Adapt the representation to the aim of your text analysis and to the question it should answer(a short text is not always the optimal solution).


Bradley, R., & Steele, K. (2015). Making climate decisions. Philosophy Compass, 10(11), 799–810.
Brun, G., & Hirsch Hadorn, G. (2014). Textanalyse in den Wissenschaften: Inhalte und Argumente analysieren und verstehen. vdf Hochschulverlag AG.
Hansson, S. O., & Hirsch Hadorn, G. (2016). The Argumentative Turn in Policy Analysis. Springer International Publishing.
Lempert, R., Nakicenovic, N., Sarewitz, D., & Schlesinger, M. (2004). Characterizing climate-change uncertainties for decision-makers. An editorial essay. Climatic Change, 65(1), 1–9.
Mueller, P. (2010). Constructing climate knowledge with computer models. Wiley Interdisciplinary Reviews: Climate Change, 1(4), 565–580.