Free Riding and Contribution | AI Overview

Question 1

Define a private good.

Answer 1

goods or services that are excludable and rivalrous.

Question 2

Define a public good.

Answer 2

goods or services that are non-excludable and non-rivalrous.

Question 3

Define excludable.

Answer 3

a good whose ownership can be restricted

Question 4

Define rivalrous.

Answer 4

an individual consuming the good diminishes the utility available to
another person

Question 5

Explain the biggest challenge faced by private provision of public goods.

Answer 5

Under contribution is a major challenge for many communities as many rely on “heavy” contributors, which is even more unbalanced in the digital economy.

Question 6

Define the free-rider problem.

Answer 6

A type of market failure that occurs when those who benefit from the resources, public goods of a communal nature do not pay for them or under-pay

Question 7

How does the Prisoner’s Dilemma explain underproduction/overconsumption?

Answer 7

The prisoner’s dilemma is a paradox in decision analysis in which two individuals acting
in their own self-interests do not produce the optimal outcome. Although there may be a socially optimal outcome, an individual may let others pay

Question 8

What is the tragedy of the commons?

Answer 8

The situation in which individuals with access to a public resource (also called a common) act in their own interest and, in doing so, ultimately deplete the resource.

Question 9

How is user participation typically represented?

Answer 9

90% are lurkers
9% contribute from time to time
1% actively participate and account for most contributions

Question 10

Give 6 reasons for contributing to public goods.

Answer 10

Pure altruism
Warm-glow giving (emotional reward of giving to others)
Fun
Reputation/Recognition
Reciprocal benefit
Explicit reward

Question 11

Define intrinsic and extrinsic motivation.

Answer 11

Intrinsic motivation involves doing something because it’s personally rewarding to you.

Extrinsic motivation involves doing something because you want to earn an external reward or avoid punishment.

Question 12

Give 4 solutions for undercontribution.

Answer 12

• Make it easier to contribute
• Reward (without over rewarding) participants
• Social Comparison
• Gamification

Question 13

Give the hierarchy of AI models from simplest to strongest.

Answer 13

Rule-based Model
Machine Learning Model
Deep Learning Model
Transformer
Large Language Model

Question 14

What are the 3 characteristics that evolve as AI models progress from simplest to strongest?

Answer 14

• Boundary between statistics and AI becomes clear
• Decreasing level of human engagement
• The black box problem becomes more pronounced

Question 15

Define Rule-Based Models, and give the pros and cons of them

Answer 15

Hard-coded “if-then” rules manually created by humans.

Pros: Simple, explainable, and resource-efficient.
Cons: Inflexible, doesn’t learn from data, limited to predefined scenarios

Question 16

Define Machine Learning, and give the pros and cons of them

Answer 16

Models that learn patterns from data using algorithms like regression, support vector machines, random forests, etc.

Pros: Adapts to data patterns, can generalise beyond training data.

Cons: Requires substantial data; results depend on data quality.

Question 17

Define Deep Learning and give the pros and cons of them

Answer 17

Neural networks with multiple layers for extracting complex patterns.

Pros: Handles unstructured data (images, audio, text), highly accurate for complex tasks.
Cons: Requires extensive computation and data, less interpretable.

Question 18

Define Transformers and give the pros and cons of them

Answer 18

A type of deep learning model using Attention Mechanism, particularly efficient in natural language processing (NLP) tasks

Pros: Processes data in parallel, excels in NLP tasks.
Cons: Computationally intensive, requires massive datasets.

Question 19

Define LLMs, and give the pros and cons of them

Answer 19

Built on transformer architecture and trained on massive amounts of text data.

Pros: Handles diverse tasks, highly flexible, pre-trained on massive data.

Cons: High computational cost, potential for biased outputs.

Question 20

Explain when to use each of the 5 AI Models.

Answer 20

Rule-based: Clear, well-defined problems with deterministic outputs.
Machine Learning: Problems with labelled/unlabelled data and measurable patterns.
Deep Learning: Large datasets, problems requiring feature extraction or complex patterns.
Transformers: Large text, sequential, or contextual problems requiring long-range dependencies.
LLMs: Complex, context-sensitive language tasks like summarisation or question answering.

Question 21

Give a use case for each of the 5 AI Models.

Answer 21

Rule-based: Automated customer support with predefined FAQs.
ML: Product Recommendations
DL: Image Recognition in medical diagnostics
Transformer: Machine Translation
LLMs: Building conversational AI