Everyone has to make decisions, every day. However, it is not always clear to us what outcomes can derive from these decisions. When this happens, we say we are making decisions in situations under risk or uncertainty. When risk appears, although we are not sure about the possible outcomes of a given decision, we do know the probability of each outcome, while in situations under uncertainty we are not capable of making such assumptions.

In this Learning Path we’ll learn bout risk and uncertainty. We will see how risk can be analysed by using expected utility instead of expected value, and how different kind of people will behave differently when facing risk.

Risk and uncertainty:

Risk, when different outcomes are unknown but individuals can assign them probabilities;

Uncertainty, when individuals cannot assign probabilities to each outcome.

Expected utility:

Saint Petersburg paradox, solved by Daniel Bernoulli;

Expected utility, an alternative way to make decisions;

Expected utility theory, developed by John von Neumann and Oskar Morgenstern.

Risk aversion analysis:

Risk aversion, where we analyse different degrees of risk aversion;

Arrow-Pratt measures of risk aversion.

In our previous Learning Path, we learned the basics about Information economics. We saw what adverse selection and moral hazard are, using as en example the healthcare insurance market. Of course, insurance companies have ways of getting around adverse selection and moral hazard to a certain extent. But an insurance company is never going to be able to segment their policies fully: when was the last time you were asked about your driving style when subscribing to a dental plan? And most sane people won’t risk their life just because their healthcare is covered (in fact, most countries with a free, national health service have longer life expectancies and shorter hospital stays than countries that don’t), but how many of us park our friend’s car more carefully because we are not insured in it?

In this LP we’ll cover the implications of asymmetrical information, looking at the most important examples. We’ll start by looking at adverse selection, then we’ll learn more about moral hazard.

Adverse selection:

Adverse selection, ex-ante asymmetric information;

The Market for Lemons, a paper describing adverse selection and moral hazard;

Screening, which employers use to beat adverse selection;

Signalling, which good employees-to-be use to avoid adverse selection.

Moral hazard:

Moral hazard, ex-post asymmetric information;

Credit Rationing in Markets with Imperfect Information, a paper explaining how moral hazard affect credit markets;

Incentives, used by employers to avoid moral hazard.

Information economics, also known as economics of information, is the study of how different degrees of information affect economic analysis. Since it’s usually studied as a part of microeconomic theory, information economics mainly deal with micro problems, although it is easy to see examples in our own life: why should I study a college degree? Should I look for a job with a fixed salary or should I go for incentives-based salaries? If I quit smoking, drinking or driving, should I stop paying healthcare insurance?

In this Learning Path we’ll learn the basics about information economics, especially about adverse selection and moral hazard, in order to make a thorough analysis in the next LP. We’ll start with:

Information economics, a basic definition.

Information:

Complete and incomplete information, their differences and how to deal with them;

Perfect and imperfect information, taking special interest into

Asymmetric information, the main reason why information economics exist.

Problems and how to deal with them:

Adverse selection, an ex-ante problem;

Moral hazard, an ex-post problem;

Agency theory, the frame we’ll use to analyse these issues.

In game theory, games are usually classified under two categories: simultaneous games and sequential games. Although simultaneous games make up for a lot of the research made during the early years of game theory, sequential games represent a closer step when modeling economic reality. Consider for instance a firm thinking about entering a new market, dominated by a monopoly. The new firm must take into account that its move (entering or not) will affect the monopolist decisions. Since both actions, the entrance and the monopolist’s reaction, don’t take place at the same moment, they are better represented by a sequential game.

Duopolies are commonly used when explaining sequential games, because they model the interdependence between two firms. We’ll learn in this Learning Path how duopolists react to each other’s actions, how collusions work and how repeated sequential games may change the essence of a game. We’ll learn about:

Sequential games, how they work and how they differ from simultaneous games.

Some basic definitions:
Subgames, which must be understood when dealing with sequential games;

Folk theorem, a necessary tool for the analysis of duopolies and collusion.

Duopolies and collusion:

Cournot duopolies, which we review to see the main differences with

Stackelberg duopolies, a true sequential game;

Collusion agreements would reduce consumer’s surplus while increasing the firm’s.

Repeated games, and how everything changes when considering repetitions.

In this LP, the second of our series on Game theory, we’ll learn everything there is about simultaneous games. These games, used when considering a game where players move or play their strategies simultaneously, are commonly used in many fields. From military strategies to collusion agreements, the analysis of these situations as simultaneous games can help us discover the best way to act. Finding Nash equilibria or knowing how to proceed to eliminate dominated strategies will get us the best outcome (and therefore help us win the game!).

We’ll consider that the rules of the game and each player’s payoffs are common knowledge, and that information is complete. Since moves are simultaneous, information will be imperfect. We’ll see the following definitions:

Simultaneous games, where we’ll learn the basics about these games.

Nash equilibria and dominant strategies:

Prisoner’s dilemma, the well-known game;

Nash equilibria, which are the best possible strategies given the other player’s;

Dominant strategies and elimination of dominated strategies, another way to proceed.

Mixed strategies:

The Battle of the Sexes, where a couple has to decide what to do over the weekend;

Mixed strategies, and how to analyse them where no clear equilibrium can be reached.

Continuous strategies:

Cournot duopolies, the best example of a simultaneous game where continuous strategies  are used.

Game theory is a fascinating part of micro, and macro, economics. It’s based on the psychological current that underpins all Economics, and simplifies the main economic problem: optimising the use of limited resources; into a series of sets of events that illustrate how this problem drives human interaction. The aim of game theory is to take simple situations as allegories of the much more complex social and economic interaction that takes place every day to explain anything from price wars to altruism.

Understanding game theory may or may not make us more effective in business, economics or our personal lives, but it undoubtedly helps us to highlight and recognise the economic (in the non financial sense of the word) patterns that underpin the better part of the way we base our societies and the way we lead our lives. The fact that many of these same social patterns occur all throughout nature also helps remind us that the most complex societies are based on behaviours and choices not all that far removed from the natural world.

In this first Learning Path on Game theory, we will learn about the main tools and conditions required in order to make a thorough analysis of games. We’ll see how the quality of information shape the way we solve games, and learn about how to describe them. We will start with a simple definition:

Game theory: what it is and how did it evolve for during the last century.

The importance of information:

Complete and incomplete information will determine the scenario we’ll work in;

Common knowledge is a notion that adds importance to complete information;

Perfect and imperfect information, which determines whether players can see each other moves.

Description of games:

Strategic form (or normal form), used mainly for describing simultaneous games using a game matrix;

Extensive form, used when dealing with sequential games, by using a game tree.

On our final Learning Path of our series on cost analysis, we’ll learn about cost efficiency, how to reduce costs while maintaining volume and quality. Therefore (and starting with economies of scale, as a link to last week’s LP), we could ask ourselves the following questions: will my costs fall if I produce more? Or if I produce a wider portfolio? Or what is the role training and specialisation can play in increasing efficiency?

After understanding how cost efficiency can be achieved, we turn to the main critiques of neoclassical cost analysis. Up to now, everything sounded just as what they’ve been teaching us at school and college. However, a few theories stood up against all this, mainly form Industrial Organization theory.

In this LP, we’ll start by looking on how to improve cost efficiency, with:

• Economies of scale, looking at how our levels of production can help us maximise cost efficiency;
• Economies of scope, looking at product portfolio size in cost reduction;
• Economies of learning, looking at the impact of know how on cost, an analysing in detail
• Learning curves and
• Experience curves.

Then, we’ll learn about the main critiques against neoclassical cost analysis:

• Industrial organization, analysing its main authors and concepts;
• George Stigler, one of the key figures of Industrial Organization;
• Stigler’s cost analysis theory, which greatly differed from classical economies of scale;
• X-inefficiency, an idea put forward by Harvey Leibenstein.

Having covered the basics of cost analysis, we are in a position to explore other important aspects of cost efficiency. For example, will my costs fall if I produce more? As economies near perfection, costs efficiency becomes increasingly crucial, so much so that investment plays a greater role. But just how and where that investment in cost management should be directed is the subject of our second LP on cost analysis. In this LP, we’ll start where we left in our previous LP:

Short run cost analysis:

• Period analysis, where will see what determines the short and the long run.
• Short run cost analysis, as seen last week, is about dealing properly with fixed costs. Continue reading →

Two things determine profits: income, or turnover (the price at which we sell something) and costs (how much we spent making what we sell).  Therefore, knowing how much our costs are going to be is essential when planning the viability of a business, especially given that in a perfect economy, price is not determined by us but by what the market dictates. This is therefore essential before we can put all these factors into place and look at the whole picture: what to produce, how much of it to produce, and whether we will make anything out of it.

In this first LP on costs, we will start by remembering what we learned a few weeks back:

• Production maximisation, then
• cost minimisation and put them together in
• production duality analysis.

Then, we’ll look at the framework in which we analyse costs: the types, what determines them and how this determines our choices regarding production levels, starting with:

• Period analysis, learning about what determines the short and long run; then move onto the basic
• types of cost, which will affect
• average and marginal costs and their importance, before putting it together in
• cost analysis in the short run.

This second Learning Path on production will draw on the basics we saw in Production I and add several extra factors to our analysis. We will basically introduce three new parameters to our possibilities: we will add a time frame and see how this shapes our choices, we will introduce the ability to produce more than one good or service, and we will also take a first look at prices, production costs and competition as a whole market dynamic.

Under perfect competition, prices are determined by market forces. If the price of a specific type of bog standard, hexagonal, 15mm screw, for example, is  0.01€, there is nothing stopping you from trying to sell it at ten times the price, but you probably won’t last very long. You’re better off deciding whether you can make a profit selling at that price before you start producing at all. In terms of labour and capital, we suppose they are interchangeable, as we saw when we looked at the MRTS. Depending on the going price of labour (wages) and capital (machinery and interest rates), you will choose one or the other or a combination of both: whatever is most productive per euro spent.

All we can now do is set our level of production. We know that marginal productivity drops with each added unit of labour (or capital), under the law of diminishing returns. When, therefore, should we stop? We could try cramming our screw factory with workers until they begin getting in each others’ way and start having negative productivity (the equivalent of getting them to work 20 hour days until they are tired enough to start doing more harm than good), but not only is this not health and safety compliant, it is also much too late. The simple answer is that we stop when it is not worth hiring any more workers. This idea of ‘worth’ will depend on various factors, and is the subject of our analysis in this second LP.

So, let’s define these factors and get started. We will begin by looking at the concept of

• Static period analysis, introducing one of the main factors we will have to juggle.

This will lead us onto our first scenario, the short run, where we will look at:

• Production functions and
• Production analysis in the Short Run

In the next section we will cover the long run, with:

• Analysis in the Long Run and
• Returns to scale

Before an entry on

• Production analysis in the Very Long Run

In the final section of this LP, we will introduce another factor, multi-product analysis, in two entries on:

• Production possibility frontier and
• Multi-product function.