Goal-Oriented Interaction and Decision Making | Interaction Design

Interaction as Search

A foundational first-wave meta-theory: user interaction can be modelled as search. Given a clear goal, the user’s interaction with an interface is a search for actions that reduce the distance to that goal. This is precisely the theoretical basis of the Cognitive Walkthrough: identify the next sub-goal → check whether the necessary action is available and recognisable → confirm the system gives feedback of progress.

This model works well for well-defined, repetitive tasks performed by expert users — precisely the domain of KLM/GOMS and CW. It works poorly for exploratory, creative, or emotion-driven interaction, where there is no stable goal to search toward.

Bounded Rationality

Classical economics assumes fully rational agents who optimise. Bounded rationality (Herbert Simon) recognises that the cost of searching for an optimal action is itself a cognitive cost. Given limited time, attention, and computational capacity, people often satisfice: choose a plan that is satisfactory rather than optimal.

For interaction design, this means:

Users do not read every menu item before choosing — they scan for the first option that looks “good enough.”
Users do not explore every feature — they learn one method and stick with it, even if a more efficient method exists.
The cost of deciding what to do is part of the task — interfaces that demand heavy decision-making under time pressure push users toward satisficing, which may produce errors.

Prospect Theory and Heuristics

Prospect theory (Kahneman & Tversky) models decisions via a utility function over outcomes weighted by estimated likelihood, rather than assuming complete knowledge. Several well-documented heuristics (mental shortcuts) and biases explain systematic deviations from optimal behaviour:

Heuristic / Bias	Description	Design Implication
Availability heuristic	Judging likelihood by what comes easily to mind — vivid, recent, or emotionally charged examples feel more probable.	A user who recently heard about a data breach will overestimate the risk of using a cloud service, even if the statistical risk is unchanged.
Affect heuristic	Deciding via emotion (“how do I feel about this?”) rather than calculated cost/benefit.	The aesthetic quality of an interface strongly affects perceived usability — users judge beautiful interfaces as more usable even when they are not (the “aesthetic-usability effect”).
Representativeness heuristic	Judging probability by resemblance to a familiar category.	A user expects medical-looking apps to be trustworthy and playful-looking apps to be less serious, regardless of actual quality.
Loss aversion	Losses hurt more than equivalent gains feel good (roughly 2×).	Framing a message as “you will lose your progress if you leave” is more motivating than “you will save time if you stay.”
Expectation bias	Seeing what you expected to see — users overlook errors that do not match their mental model.	A button labelled “Submit” that actually deletes data will be clicked without scrutiny because the label matches the benign expectation.
Bandwagon effect	Preferring what others have chosen — social proof.	Showing “10,000 people chose this plan” influences user choice more than a feature comparison table.

Attention Investment Theory

Models end-user programming and automation decisions. Automating a repetitive task (e.g. writing a regex find-and-replace, recording a macro, configuring a filter rule) offers future time/attention savings but costs present time/attention to specify, and carries the risk of “bugs” that cost more time to fix.

The utility comparison is: $\text{benefit} = \text{future attention saved} - (\text{present cost of specification} + \text{risk of costly errors})$

This explains a common observation: many users persist with manual, repetitive actions rather than learning an available shortcut or automation feature. From the user’s bounded-rationality perspective, the expected value of investing the attention to learn the shortcut is negative — the time saved per use is too small relative to the upfront learning cost and the risk of getting it wrong.

Design implication: to encourage adoption of efficiency features, reduce the specification cost (make the feature discoverable and its use obvious), reduce the error risk (preview the result before applying), and ensure the per-use saving is large enough that users perceive the investment as worthwhile.

Persuasive Design and Nudges

Persuasive design uses knowledge of cognitive biases to influence user behaviour — often toward goals the user themselves has endorsed (exercise more, save money, reduce energy consumption). However, this raises ethical concerns:

Nudges work by exploiting biases (default options, social proof, framing effects) without the user’s conscious deliberation.
Users who recognise they are being nudged may react negatively — perceiving the design as paternalistic or manipulative.
If the designer’s goals diverge from the user’s (a “dark pattern” — e.g. making it easy to subscribe and hard to cancel), persuasive design becomes deceptive.

The ethical boundary: persuasive design is legitimate when the designer and user share the same goal and the nudge is transparent; it becomes manipulation when the designer pursues their own goal at the user’s expense.

Limits of Goal-Based Models

Goal-based models (interaction as search, KLM, GOMS, CW) all share the fundamental assumption that the user has a stable, well-defined goal. This assumption breaks when:

The user is exploring with no fixed goal (browsing a social feed).
The user’s goal changes during the interaction (discovering a feature they did not know existed and pivoting).
The user’s goal is emotional or experiential (listening to music for mood regulation — “efficiency” is irrelevant).
The user does not know what goal is realistic (a novice facing a complex tool — they cannot search toward a goal they cannot yet formulate).

For these situations, the earlier methods (ethnography, reflective design, iterative prototyping, participatory design) are more appropriate than task-based methods. Knowing which limitation applies to which system, and switching methods accordingly, is the core skill the course tests.

Expected Learning

Explain the interaction-as-search model and which theoretical methods (CW, KLM, GOMS) depend on it.
Define bounded rationality and satisficing, and explain their design implications.
Name at least four cognitive heuristics or biases and give a design-relevant example of each.
Explain Attention Investment Theory and why users persist with manual methods rather than adopting shortcuts.
Distinguish legitimate persuasive design from deceptive dark patterns.
Recognise when goal-based models are inappropriate (exploratory, emotional, or wicked problems) and recommend alternative approaches.

Past Paper Questions

Bounded rationality, heuristics, and decision-making appear in the context of explaining why users behave as they do: why they miss errors (expectation bias), why they judge beautiful interfaces as more usable (affect heuristic), why they do not adopt shortcuts (Attention Investment Theory). This material is the theoretical underpinning of many design-critique answers.