Optimization
Optimization means choosing how to use limited resources to meet an objective under constraints. The best choice depends on what is being valued, which options are available, and how conditions may change.
THE IDEA
Optimization
The most important optimization is to do the right thing. A close second is to do the thing right.
—Jon Bentley
Optimization is about making the most of what you have. Packing luggage is a small example: you arrange a limited space to fit the things you want to carry. The best arrangement depends on your objective and constraints.
In economics, optimization is crucial for understanding how and why people make allocation decisions. Many economic theories assume that people will always seek to gain the maximum possible benefit from deploying their resources—they “do the best for themselves given how much money they have and how much things cost.” Of course, everyone has a different optimization formula, because everyone places different values on things. And we’re never optimizing in only one dimension. We may want to get good value when we spend money, but not at the expense of wasting our time. That balance is subjective: for some, spending an hour to save a dollar is worthwhile, while others would rather forgo the dollar than lose the hour. Companies also optimize across multiple dimensions—pursuing profit while juggling employee satisfaction or brand reputation.
The concept surfaces in many fields. Businesses optimize supply chains, coders optimize algorithms, mathematicians explore optimal points (like global and local maxima), and biologists trace how species have evolved to fit their environments. Some economists even see the economy itself as an evolutionary system. In a changing environment, a successful arrangement may need adjustment. Experiments can reveal better options, but trying new combinations also carries risk.
Biological evolution illustrates the limits of specialization. A trait that helps an organism in one environment may become a disadvantage when conditions change. Imagine an insect that evolves a feature allowing it to drink nectar from a single flower. The specialization may help it compete while that flower thrives. If climate change wipes out the flower, dependence on it becomes a liability.
Companies face an analogous conundrum. Optimizing ruthlessly for today’s environment can corner a market, but it may also leave a firm unable to adapt when the market shifts. To survive and evolve, businesses need to keep some options open—diverting resources from immediate needs to invest in the ability to play the long game. As Eric D. Beinhocker explains, there is a constant tension between “how much should be invested in executing for today versus adapting for tomorrow.” A business therefore needs to consider both current performance and its ability to adapt.
For individuals, optimization criteria also change over time. A fancy car might feel optimal today for the status it brings, but spending more than necessary now may shrink your options when it’s time to move into a retirement home. Factoring future needs into today’s decisions inevitably alters how we allocate resources. And in novel economic situations, we can’t predict what will be optimal; we must experiment, trying multiple formulas and accepting risk. Optimization is not a promise of getting 100 percent of everything we want. We exchange income for goods and time for experiences, negotiating these decisions while billions of other people make their own choices. As Beinhocker writes, “human beings will seek their maximum happiness state within the constraints of their finite resources and will trade their way to get there.”
Because everyone has a different optimization formula, it’s very hard to evaluate others’ choices. Beware of judging someone’s decision as suboptimal without knowing what they’re optimizing for. If you saw the world through their eyes, you might make the exact same trade-offs. Finally, exercising choice presupposes having choices, which isn’t always true. Even with the same amount of money, not all economic agents share the same set of options. Culture, laws, and social norms heavily shape the range of possibilities for optimization.
The set of choices that individual and collective agents face, however, is far more complex than that implied by economic models of utility maximization that assumes perfect information, exogenously given preferences, and sovereign self-interest.
—Nancy Folbre
When to use it
Personal finance: allocating income between immediate desires and long-term savings.
Business strategy: deciding how much resource to invest in current products vs innovation.
Supply chain management: optimizing inventory levels to minimize cost while meeting demand.
Software development: optimizing code for speed vs readability.
Allocating a limited budget across multiple projects to maximize return.
Planning a daily schedule to minimize wasted time.
Designing a workout routine that delivers maximum fitness gains with minimal time investment.
Streamlining a business process to reduce costs while maintaining quality.
FROM THEORY TO EVERYDAY LIFE
In practice
Packing luggage
Finding the best way to arrange everything so that it fits perfectly, minimizing wasted space and maximizing what you can carry.
Specialized insect and flower co-evolution
Imagine an insect adapted to drinking nectar from a single flower species. That specialization may help it feed efficiently, but dependence on one species becomes dangerous if the flower disappears.
Buying a fancy car vs. saving for retirement
Spending heavily on a status symbol optimizes for immediate social rewards but may restrict funds for later needs like a retirement home, illustrating the tension between short-term and long-term optimization.
Student Skipping Steps
A student learns a shortcut for a familiar type of math problem. If the student never learns why it works, they may apply it to a new problem where its assumptions do not hold. Saving time on familiar exercises can come at the expense of adaptable understanding.
Just-in-Time Inventory
A company uses just-in-time inventory to reduce storage costs. Smaller buffers can leave it more exposed to supply interruptions, so its inventory objective must account for both carrying costs and disruption risk.
A MOMENT TO REFLECT
Put it to work.
What are you optimizing for in your current life, and how might that change over time?
Can you identify trade-offs you're making that might not be optimal if your environment shifts?
When judging others' decisions, how can you better understand their unique optimization formula?
What constraints limit your options even when you have enough money to afford a choice?
When have you fallen into the trap of optimizing at the expense of robustness?
How much current performance would you give up to preserve an option that might matter later?
Often confused with
References
- Note 1 — Jon Bentley
- The Origin of Wealth — Eric D. Beinhocker (2006)
- Unknown — Nancy Folbre
CONTINUE THE CONVERSATION
Source texts
- The Great Mental Models Volume 4 Economics and Art
- The Great Mental Models Volume 4 Economics and Art