Harry Burdon

Asymmetric information arises when one party knows more than another. Understanding the implications of this will help you understand everything from why private health insurance is difficult to why socialising with new people is hard.

Asymmetric information occurs in a range of contexts:

• the buyer doesn't know if your car has a hidden fault
• the health insurer doesn't know if you have a hidden condition
• your car insurer doesn't know if you bother locking your car
• your boss doesn't know if you're working or scrolling Tiktok
• you don't know whether or not your new acquaintance is a weirdo

The first three are hidden facts. The second two are hidden choices. Economists study these situations because they can lead to markets unraveling and collapsing. There are also a range of ways to mitigate them including: warranties, references, qualifications, reviews, medical questionnaires, insurance excesses.

For a seminal paper on this issue, see: The Market for "Lemons": Quality Uncertainty and the Market Mechanism (1970)

Causality is a branch of statistics which economists specialise in. It lets us study all kinds of interesting questions, like the impact of minimum wage on jobs, an extra year of education on income, whether a drug works, smoking bans on smoking rates, pornography on sexual assault.

Let's talk through causal analysis intuitively as well as nerding out on causality theory, e.g. the work of Judea Pearl.

See A Crash Course in Good and Bad Controls (2022), The Book of Why (2019) and Causality (2009).

Regression analysis is one of the most common tools in statistical analysis. It can be beautifully simple or very complex.

I can explain how it works in simple terms for people with zero background in statistics.

My favourite book on this, a hidden gem: Regression Analysis: A Practical Introduction by Jeremy Arkes

Economic models are often criticised for relying on absurd assumptions, such as perfect rationality. Despite this criticism, these models persist throughout our politics, policy, and in business.

If modelling is the art of simplifying a complex world through assumptions and abstractions, the question is not if these assumptions are true, but whether they are useful. What makes a good assumption? When does a simplification improve understanding vs worsen it? Let's discuss how economists approach these questions, from traditional modelling methods, to more recent developments in behavioural economics that adapt the assumptions with psychological research.

For further reading, Milton Friedman’s The Methodology of Positive Economics (1953)

Machine learning and AI algorithms are used for decisions that impact people's lives, such as assessing insurance risk, credit approval, and screening job applications. And these algorithms can sometimes discriminate against protected groups.

I am particularly concerned about proxy discrimination, where seemingly innocuous characteristics (e.g. postcode, size of car engine, etc) act as stand-ins for protected characteristics (e.g. race, sex). Even when protected characteristics are excluded from models, they can still discriminate via the proxies.

As a professional in economics and causal inference, I break this problem down through the lens of causal analysis. By understanding how inputs causally impact a model's outputs, we can assess if and when discrimination arises.

Further reading:

• Implementing Anti-Discrimination Policies in Statistical Profiling Models (2011)
• Reuters: Insight - Amazon scraps secret AI recruiting tool that showed bias against women (2018)
• Discrimination-Free Insurance Pricing (2021)

Economic theory often seeks to identify a model's "equilibrium" - a stable state towards which the systems trends and then rests. However, many economic, political and social systems have multiple equilibria. This provokes questions: which equilibrium will emerge, what determines that outcome, and how stable is it?

Understanding this illuminates the robustness and fragility of the systems on which we rely. It also explains why seemingly arbitrary features can be highly influential.

For example...

• Why a bank can be healthy one day and collapse with a bank run next.
• How and why power within an organisation can be concentrated on a single individual.
• Why political norms can remain entrenched for decades until suddenly they are not.
• How your life could change from going great to collapsing quicker than you hoped (and, more optimistically, vice versa).

Statistics - both wonderfully insightful and easily to screw up.

Let's discuss what statistical estimates actually tell us:

• What's an "average treatment effect"? When's it meaningful vs not? FYI, half the statistics you've ever seen are this.
• What actually are statistical significance and p-values? A favourite tool of statisticians, and misunderstood by 95.6% of individuals.
• What makes the results of a regression model meaningful? Understanding how to make sense of the shape of data and fitting a linear model.
• Getting from correlation to causation. The various techniques in causal inference.
• Does the context of the stat apply to your context? Understanding external validity.

For further reading (aimed at those with a somewhat quantitative background), this little known gem: Regression Analysis: A Practical Introduction by Jeremy Arkes

How to design systems to allocate scarce resources. Thinking through individuals’ incentives and behaviour, and how outcomes differ under different mechanismas. Discussing different economic frameworks, including  tradable permits, quotas, auctions, and financial instruments. Assessing these frameworks under various social welfare functions (i.e. aggregating individual well-being into a collective measure of societal satisfaction). Applying mechanism design to various problems.

Standard models (prisoners’ dilemma, battle of the sexes, one-shot games, repeated games, etc), their application to local systems, economic, political, social and global settings. Also evolutionary game theory, e.g. how altruistic behaviour can dominate in a population, even in an environment where in head-to-head interactions, egoists dominate altruists, see Is altruism evolutionarily stable (1994)?

Different objectives of voting systems, cardinal and ordinal ranking systems, the challenge of aggregating preferences across a group into a single preference, pros and cons of different voting approaches across various contexts. Applications of voting systems for preference aggregation outside of traditional elections, e.g. business decisions on budget allocation between divisions, aggregating diverging outputs across AI agents into a single answer, problems the cohort is thinking about, etc.

See https://plato.stanford.edu/entries/voting-methods/