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Practical Reasoning (Goal-to-Action)

Also Known As: deliberative reasoning goal-to-action reasoning decision-theoretic argument
Argumentation Scheme ID: practical_reasoning

Definition

Practical reasoning (goal-to-action) is the extended form of means-end reasoning that explicitly considers the agent's goals, available actions, their consequences, and values to arrive at a decision about what to do. Unlike simple means-end reasoning, this more comprehensive version considers multiple goals that may conflict, side effects of actions, and whether the goals themselves are worth pursuing. It is the fundamental framework for deliberative decision-making.

Examples

We want to reduce childhood obesity (goal). We could ban sugary drinks in schools (action 1), fund nutrition education (action 2), or subsidize healthy school lunches (action 3). Each has different costs, implementation challenges, and effectiveness profiles. Considering our budget constraints and evidence base, subsidizing healthy lunches is the most effective and politically feasible option.

Our startup wants to increase monthly active users by 30% in six months (goal). We could launch a referral rewards program (action 1), invest in targeted social media advertising (action 2), or partner with complementary apps for cross-promotion (action 3). The referral program costs little but grows slowly; ads are fast but expensive; partnerships offer reach but require negotiation time.

The city council wants to reduce downtown traffic congestion by peak hour (goal). Options include expanding bus routes (action 1), introducing congestion pricing (action 2), or building a new park-and-ride facility (action 3). Bus expansion improves access but takes years; congestion pricing works quickly but faces public resistance; park-and-ride helps commuters but requires significant land acquisition.

Verification Steps
Verification Steps
Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.
Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

Binary (yes/no) questions an LLM must answer to identify this aspect:

  1. 1

    Is a specific goal or desired outcome being identified?

    Type: binary
  2. 2

    Is the proposed action actually a viable means to achieve the goal?

    Type: binary
  3. 3

    Are there side effects or alternative actions being overlooked?

    Type: binary
  4. 4

    Is the goal itself justified or desirable?

    Type: binary
Deep Dive
The expandable detail section on each aspect page with examples, psychology, and counter-strategies.
The Deep Dive section provides in-depth information about each aspect: a real-world example showing the pattern in action, an explanation of why it works psychologically, practical advice on how to counter it, alternative names, and links to related aspects.