The focus of the afternoon is Argument Analysis applied to environmental decisions, specifically Climate Change.

Part 1 – Modelling:

Understanding modelling by the example of the oblique throw in football:

A target system describes what we want to achieve (e.g. getting the ball to a specific location). Based on the target system a conceptual model can be devised. Specifically, we idealise properties that allow us to model the specific target system (weight, but not colour).

Model equations (e.g. Newton’s laws) are selected, parametrized (e.g. wind) and implemented. The implementation must consider numeric code, parameter values, initial conditions and boundary conditions.

Running the implementation produces a simulation results which is  deterministic *).

The problems focus on structural model uncertainty (uncertainty of numerical code, parameters, initial conditions and boundary conditions) and deductive uncertainties.

*) Up to randomization and parallelisation effects.

Part 2 – Argue with uncertain information:

An important logic puzzle to consider is the Wason Selection Task as it shows difficulties with reasoning, even in relatively simple case.s

We argue if it is controversial whether a statement is true or not. Further we want to show that our reasoning is sound. Most arguments are deductive, but also non-deductive arguments are possible.

The standard form of arguments is inference. Premises are used to justify a conclusion. The correctness of premises as well as the inference is debatable and needs to be confirmed to accept the conclusion.

Argument analysis begins with a complete arguments and follows these steps:

1. Reconstructing arguments: Identify premises and conclusions in the argument.
2. Evaluating arguments: Are premises true or not and can the relationship between premises and conclusion be proven correct? Deductive arguments can be formally verified, whereas in non-deductive arguments there validity cannot be confirmed by correctness. Premises do not guarantee the conclusion  (i.e. they are probabilistic). Fallacious arguments can follow if premises are too weak to support the conclusion. Weakness can be caused by critical points:
   • Inductive inferences
     • incomplete information
     • sample sizes, representativeness
     • Wrong intuitions about probability
   • Causal arguments
     • Inappropriate concept of causality (single/multiple causes; feedback)
     • Incomplete information
     • inference from mere positive correlation, or temporal sequence
   • Arguments by analogy
     • Incomplete Information
     • Illustrative or relevant (dis-)analogy of different strength

A (logical) side note:

On sufficiency and necessity:

In the assertion of the form “If A, then B”:

• A (true) is a sufficient condition for B (true) .
• B (true) is a necessary condition for A (true): “If not B, then not A”.

The assertion “If and only if A, then B”:

• A is sufficient and necessary condition for B, and vice versa.

In an assertion of the form “Only if A, then B”:

• A(true) is a necessary condition for B (true)
• B (true) is a sufficient condition for A (true). “If not B, then not A”.

On conditionals:

(to be filled in)