For as long as people have roamed the earth, there has been a fear of strangers. The term xenophobia comes from Ancient Greek and combines xeno (meaning foreign or alien) and phobia (meaning fear). In particular, it is common for natives of a country – whether today or in the distant past – to worry about immigration, especially illegal immigration.

In times of turmoil, one would think that a stable, or relatively stable, exchange rate would be a boon to policymakers, soothing the anxieties of international investors. However, keeping the value of the currency stable against a foreign currency such as the US dollar, when buffeted by shocks, entails sometimes painful tradeoffs.

The exchange rate is the key relative price for an economy open to international trade and finance. The price Americans pay for Japanese automobiles imported into the United States depends on how many dollars it takes to buy 100 yen, i.e., the dollar/yen exchange rate. The stronger the dollar, the more yen it will buy, and therefore the cheaper the imported cars will be. At the same time, a strong dollar makes it harder for US firms to profitably sell heavy earth-moving equipment like bulldozers (say those made by Caterpillar) to the rest of the world. The strength of the dollar against other currencies also affects other sectors, besides trade in manufactured goods. A strong dollar is good for a US tourist visiting Madrid, but places in the United States that cater to foreign tourists, such as Las Vegas, do better business when the dollar is weak. And whether one decides to purchase stocks and bonds denominated in dollars, as opposed to, say, German securities denominated in euros, has a great deal to do with how one expects the dollar to move against the euro over time.

The simplest and most scalable type of optimization problem is one in which the objective function and constraints are formulated using the simplest type of functions – linear functions. We refer to this class of problems as linear optimization (LO) problems.

Russia’s invasion of Ukraine in February 2022 sent shock waves though the world’s wheat market. The world price of wheat jumped from about $8 per ton to more than $13 per ton within a few days. The markets feared that wheat supplies from the region – which account for a third of the world’s wheat harvest – would be disrupted.

This chapter critically discusses the exponential discounted utility model (EDU). In the class of discounted utility models, the EDU model uniquely gives time consistent choices. The psychological foundations of EDU are quite limited, and captured by a single parameter, the constant per-period discount rate. The EDU model is rejected on several grounds. Unlike the assumption of constant discounting, as an outcome is brought towards the present, the discount rate increases, making individuals more impatient, and giving rise to present-focussed preferences. This leads to preference reversals and the common difference effect. Furthermore, animal and human evidence shows that the pattern of temporally declining discount rates is hyperbolic, giving rise to hyperbolic discounting. Other anomalies of EDU that are considered include: Sign effect, magnitude effect, sub-additive discounting, intransitivity of preferences or cyclical preferences, and dependence of utility on shapes of consumption profiles with identical EDU.

This chapter concentrates on classroom structures that a teacher can employ, including how the room can be arranged, physically and structurally, to maximise engagement for all students. We will examine the research on learning space architecture, the role of desk configuration, group workspaces, chill-out zones and ideas for wall displays.

Structurally, we explore the use of routines in class for maintaining consistency and predictability. Examples include managing entry and exit to class, transition between learning activities and routines for what to do when students finish work, arrive late or need to use the toilet.

It is very satisfying to teach in a classroom where students are actively participating in discussions, group projects and other activities. Learning spaces are complex – both teachers and students experience numerous pressures, wants and needs that accompany them into a classroom. For instance, both teachers and their students want to be heard, to learn, to be safe and to have positive relationships with their peers, just to name a few. However, the value and sources for satisfaction that you and they place on these needs and wants at any given time may be different from one another. You may want to get on with a brilliant geography lesson, while a sleep-deprived student may just want a bit of rest and believe the right place for it is the very same geography lesson. These possibilities remind us that your lesson is taking place in a social environment with multiple stakeholders actively reacting to each other. This is why it is very important to develop strategies that will help you manage both your and your students’ expectations in the classroom. This chapter focuses on how the use of rules and expectations lays the foundations for positive and engaging learning environments.

In 2014, Fabrice Brégier, then chief operating officer of Airbus, called for the European Central Bank to intervene as the strength of the euro was “crazy.” He wanted them to push it down against the dollar by 10% from an “excessive” $1.35 to between $1.20 and $1.25. We learned in Chapter 5 how a strong currency makes it harder for domestic manufacturers to export goods, so we can understand why a European executive trying to sell commercial airplanes might worry that a strong euro was making his job harder. And it is a fact that in 2014, Airbus was registering disappointing sales compared to its rival across the Atlantic, Boeing. But why would it be “crazy” for the euro to be worth $1.35, and yet normal and acceptable for the euro to be worth 10% less than that? And how did Fabrice Brégier expect the European Central Bank to adjust the euro’s value, when the euro is under a floating, rather than a fixed, exchange rate regime?

This chapter introduces robust optimization (RO), where we aim to solve a MILO in which some of the parameters/data can take multiple (possibly infinitely many) values and we want the optimal solution to perform “the best possible,” assuming that the unknown problem parameters can always turn out to be “the worst possible.”