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:

**Reconstructing arguments:**Identify premises and conclusions in the argument.**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

- Inductive inferences

**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)