A concept that comes up pretty often in decision research is the one of aspiration levels. They are meant to reflect some kind of preference levels, meaning levels of attributes that the decision maker would like to have in an ideal situation. The idea behind the concept is that such levels can guide both the decision maker and the analyst to look for portions of the alternative space that’s relevant – better to search close to the optimal levels.

Now that’s nice and all, but for practical purposes I think an inverse concept is perhaps even more useful. By inverse I mean rejection levels. Or, as I like to call them, what-the-hell-I’m-absolutely-not-willing-to-accept-that levels. The idea is simple enough: rejection levels signify the worst attribute levels you’re willing to accept. A value worse than that means you’ll discard it immediately and look elsewhere.

The benefit is that if you have many alternatives, rejection levels can be used to make the search space smaller very fast. Imagine you’re buying a bike, and there are two criteria: cost and quality. You probably have some aspiration levels – the ideal bike. That’s reflected in the upper left corner (low price, terrific quality). But that only tells us the portion of the search space with the best alternative, but unfortunately very likely a non-existing one. Looking at the picture below, it’s clear there’s still a lot of search space remaining.

The trick with rejection levels is that you need to set them before looking at the options. A bike can be bought without issues, but any more complex decision and trouble arises. For example, house buying is of considerable difficulty in itself. And what marketers know is that if the house makes a good first impression, you’re likely to start coming up with reasons for why that house was just so lovely, convenient, and so on. As a result, people tend to exceed their budget after falling in heavy with a single house.

To avoid this, rejection levels are a great technique. If the price goes above the rejection level, you can confidently say thanks, but no thanks and just move on. By making the rejection decisions with a rule that you’ve committed to beforehand is much, much easier than mulling over each and every option you come across.

Discussions around decision making often tend to lead to the question “How can I leverage this in my own life?” Unfortunately, behavioral results are not the easiest to apply in the everyday. Sure, knowing about biases is good, especially when you’re making that big decision. But in all fairness, loss aversion or the representativeness heuristic are not usually the biggest worries.

For me, personally, the biggest worries revolve around one question: Is this really worth it? And no, I don’t mean that my mode of being is an existential crisis. What I mean is that I often find myself asking whether this particular activity is worth my investment of time and energy. This meta-level monitoring function is a direct result of the two following concepts.

Opportunity cost


If you’ve studies economics or business, you’ve surely heard of this. If you haven’t – well, you might be missing one important hammer in the toolbox of good thinking. As a concept, opportunity cost is really simple. The opportunity cost of any product, device or activity is what you don’t get instead. For example, if I go to the gym for an hour, I’m giving up the chance to watch an episode of House, for example. Of course, there are all kinds of activities one is giving up for that hour, but ultimately what matters is the best opportunity given up – that’s the opportunity cost.

Why I consider this to be important is that it’s the ultimate foundation for optimization. When one thinks about activities in terms of opportunity costs, it makes concrete the constraint that we all experience: time. No matter how rich or powerful you are, there’s always going to be that nagging limit of 24 hours a day. So it pays to think about whether something is really worth your precious time.

Marginal benefit

Marginal benefit (or utility) is also quite simple. The marginal benefit of something is the benefit you get by consuming an extra unit of that good. For example, at the moment of writing this, the marginal benefit of a hamburger would be quite high, since at the moment I’m pretty hungry. What’s important is that the marginal benefit changes over time – it’s never constant. One burger is good, and two maybe even better, but add more and more burgers on my desk and I’ll hardly be any happier. In fact, anything over three burgers is a cost to me, since I can’t possibly eat all that – I’ll just have to carry them to the garbage!

What makes marginal benefit powerful is the idea that even though I’m enjoying something, it doesn’t mean I should take in all that I can. A night out is great fun, but perhaps after a few pints the marginal benefit often plummets quite fast – you can try this by staying in the bar for extra two hours next time. Just remember to evaluate the situation next morning! ;)

These two concepts help you to ask two things. How much are you getting out of this? What could you get instead? And if the answer is that there’s something more you want instead –well, that’s a wonderful result! At least now you know what you want! :) Or, well, until the marginal benefit decreases, at least…

A naïve view of public policy (like mine, for example) might be that a body of public servants gets a set of options from the parliament, studies them and their effects, and then returns a report replete with recommendations about what the outcomes of each of the legislative options might be. A good report would say clearly “If you do this, you get A. If you do that, you get B.” The parliament’s job then is to reflect on this information and decide on the tradeoffs that the nation should accept.

In reality, however, I feel that instead of choosing from a set of options, a lot of public policy seems to be looking at options one at a time, instead of choosing the best one from a set. Suppose the economy is doing badly, and we would need either to get that back on the track, or cut costs from government. An exchange might go like this.

- Parliament: So maybe we can raise taxes?
- Right wing: NO!

- Parliament: So cut benefits to lower costs?
- Left wing: NO!

- Parliament: Reduce work legislation to increase efficiency?
- Unions: NO!

…and so on. Instead of going “OK, we have to do something, and we have options A, B, C and D”, politics employs a method I call piecewise running into a wall: evaluating one option at a time, with each being rejected by some advocacy group.

Since there is an advocacy group for almost anything, presenting options in this piecewise fashion means they will all get rejected. Following the rule “don’t do anything someone might object to” is not good policy-making: it just ensures nothing at all will be done. What is needed is a comparison of options, and then deciding which of them is the best one.

On the other hand, presenting options as a list and saying we need to choose one of them – well, that’s one of the oldest political tricks in the world. There’s nothing better than creating a false dilemma, asking a voter to pick whether for cutting taxes or reducing prosperity. Or whether he supports corporate rights or human rights. And so on.

A crucial question emerging from this is: who generates the options, and how? Letting a small group generate them invites the false dilemma trap. Getting to choose the options means you have a lot of power. Your decision may surprisingly much depend on the options that you are given. However, letting the public generate the options directly is unlikely to work, either. Most people do not know enough about the complexities of law to be able to do that. If you asked me how unemployment benefits should be structured, I would have some kind of opinion, but the opinion is way too vague to be an option directly. That’s why we need public servants and assistants in the parliament: somebody needs to generate the actual legal text.

But one thing seems clear: openness and direct communication about our options would be good for democracy. Lobbying is small in Finland, but likely to increase in the future. The more opaque the process of option generation, the more power is given to the lobbies. If politics would be more transparent, it would be harder for lobbies to slant the option set badly. But not knowing the option set, or pretending there are no other options – that’s no good. Not for us, not for the nation, not for anyone.

Why is decision analysis a good idea in the first place? Why should we focus on making some decisions supported by careful modelling, data gathering and analysis? Here, I provide some arguments as to why decision analysis is beneficial. Of course, not all decisions benefit from it: some considerations are too unimportant to warrant much analysis, and some might be simple enough to not need it. But then again, many problems are important, or complex, or politically hot. For these problems, decision analysis can be especially beneficial.

Identification of the best alternative

The main point of decision analysis (DA) is of course to arrive at the best possible alternative, or even a “good enough” one. This is essentially the focus on most discussions of DA, and therefore I won’t dwell on it more. How to determine the best feasible option is a very hard problem in its own right, deserving a book of its own.

Identification of objectives

Book examples of decision analysis start from a defined problem, and the point is to somehow satisfactorily solve it. Reality, however, starts from a different point. The first problem in reality is defining the problem itself. In fact, as a few classic books in DA emphasize, formulating the problem is one of the hardest and most important steps of DA. Much of the benefit of DA comes from forcing us to formulate the problem carefully, and preventing us from pretending to solve complex dynamic issues by intuition alone.

Creation of new alternatives

Many descriptions of DA also assume that the alternatives are already there, and that the tricky part is comparing them. Unfortunately, in actual circumstances the decision maker or his supporters are commonly responsible for coming up with alternatives, too. This is likewise critical for success, because alternatives that won’t be chosen include the ones you didn’t think of – no matter how good they would be. Duke University’s DA guru Professor Keeney has emphasized this heavily.

Analysis of complex causal relationships

It goes without saying that many issues are complex and difficult to solve – that’s why decision analysis is used, after all. A benefit of thinking the model properly through is that it can reveal some of our unvetted assumptions, even radically changing our perception of an issue. For example, I was once involved in a project setting up a new logistics center for a company. Their goal was to increase customer satisfaction by shorter delivery times. After careful analysis it turned out that the new center wouldn’t reduce delivery time by very much. So someone thought that “wow, delivery time must be really important for the customers to warrant this” and looked up the satisfaction survey data. Well, it turned out it wasn’t very important: current deliveries were well within the limit defined by customers as satisfactory. In fact, it was clear from the surveys that to increase satisfaction they ought to be doing something else entirely, like improving customer service or product quality! It sure was an interesting finding, but it took some time to convince the directors that logistics really wasn’t their problem.

Quantification of subjective knowledge

Somewhat related to the previous example, many analyses come up against the problem of uncertain or vague knowledge. Especially organizations have a habit of being full of people very knowledgeable about the business and its environment, but this knowledge isn’t really anywhere for the development department to use. It seems to go something like this. First, the analyst finds out he needs some data on, say, failure rate of delivery cars. The analyst asks a Business Development Manager, who doesn’t know anything, and tells the analyst to use some estimate. The analyst doesn’t know anything either, so ends up interviewing some delivery people, uncovering subjective, unquantified knowledge about the actual failure rate. There’s nothing wrong with subjective knowledge, it’s just that it’s of no use if the DM isn’t aware of it! By uncovering and quantifying subjective knowledge in the organization, the analysis can actually benefit the company very much also in the long term, since now they have even more knowledge to base their future decisions on.

Creation of a common decision framework

Speaking of the future, one final benefit of DA is that it provides the decision maker with a decision framework, a model to replicate next time when faced with a similar decision. This is especially beneficial in organizations, since they get stuck in meta-level issues: arguing about how to make decisions in the first place.

In the best case, DA can provide an almost ready-made framework to follow, so that the managers can focus on actually making the decision. However, it’s important to recognize that different decisions have different stakeholders and take that into account. For example, a new logistics center may be an issue mostly about operational efficiency, but a new factory demands the inclusion of environmental and labour organizations. Just taking a previously used DA framework does not ensure it’s a good fit with the new problem. But the DA frame can be something to start from, which can help in reducing political conflicts between stakeholders. In fact, there’s nothing to prevent using DA from different perspectives. For example, DA has been used successfully in problems such as oil industry regulation, or moving from a segregated schooling system to a racially integrated one. Both politically hot examples can be found in Edwards’ and von Winterfeldt’s classic.

I guess if you wanted to summarize the benefits of DA in a sentence, it could look something like this: creating structure and helping to use it. So, in fact what it does is it helps us to think better as we are forced to consider things more thoroughly and explicitly. It’s a methods that helps us to deal with uncertainty and still make a decision.

The framing effect is probably one of the best known – and also one of the most interesting – biases due to its generality, hence today’s topic. Let’s start with a classic example from a classic paper:

As a decision matrix, the situation looks like this:

As it has been formulated, there is obviously no correct answer to the question – the two options are statistically equal. What framing is about is that the way the situation is described influences our decision. If we formulate the question in terms of dead people (with the same cover story):

The formulations, as one can from the tables see, are equivalent. The surprise is that people made different choices in these situations. In the first case, 72 % chose plan A, 28 % chose plan B. With the second formulation, however, only 22 % chose plan C (equivalent to A) and 78 % opted for plan D! If framing had no power over us, we would choose the same option in both cases. So it’s not that choosing A or B per se would be irrational, it’s that making a different choice just because of framing is not rational.

The classic example is not a very natural example, however. I certainly hope I will never come across a similar situation! Thankfully, there are also more down-to-earth examples about framing. For example, suppose you are looking for some new dinnerware to buy. Visiting a flea market, you find a nice set of 8 dinner plates, 8 soup bowls and 8 dessert plates. You consider that the set is worth about 32 dollars.

As you’re just about to close the sale, the owner of the dinnerware suddenly remembers that “Oh! I just remembered! I also have some tea cups and saucers for the set!”. She adds 8 cups and 8 saucers to the set. Inspecting them, you notice that 2 of the cups and seven of the saucers are broken. How much are you willing to pay now?

Now, rationally, the set if of course worth more: after all, you get an intact saucer and 7 teacups on top of what you had before. At least it cannot be worth less – you could just throw away the additional pieces (let’s assume no costs are imposed on you by getting or disposing the broken pieces).

In fact, what happened in the experiment in Hsee (1998) was the following. Those who did joint evaluation, ie. they saw both sets (with and without broken pieces) reasoned just as we did above. The set including broken items was worth a little more. In contrast, those doing separate evaluation, ie. seeing only one of the sets, considered the second set to be worth less! In their mind, they compared it to a completely intact set, and thinking “oh, but this has broken items”. Those seeing the smaller, but completely intact set, reasoned “ah, it’s all intact and therefore good”. So a different frame generated a different evaluation of the intact pieces’ worth!

You could argue that the separate evaluators were doing their best – they didn’t know about the option of a similar set with additional pieces. And of course, that is correct. However – and this is why framing is such a sneaky bias – real life consists mainly of separate evaluations. In a store you just get to see that item with some strategically chosen comparison items next to it. When evaluating a business project, you’re mostly stuck with the description that the manager offers.

The only advice I can give about framing is that awareness matters. For example, I’ve come across situations at work when someone is asking me to do a small thing, and I’m thinking if I ought to do it now, or perhaps later. What has helped me to think is recognizing that the simple now/later is just one decisions frame. Often, I felt it’s better to back up to a wider frame and ask myself what I should be focusing in the first place. Sometimes, it turns out that I ought to do something that’s much more vital than the request. And on other occasions, when there are no other critical tasks, it’s perhaps just better to get it done right away.

So, even if I’m repeating myself a bit from last week, it’s a good idea to think about the alternatives at hand – and then question them. Are these really the alternatives? Is there a wider frame with other options? And is the description of the alternatives the only and the most relevant one?

So life is not exatly “What You See is What You Get”. It’s more exactly “What You See is What You Think You’re Getting”. Reminds me of this movie (and notice that Neo didn’t really reflect much on the frame he was given):

Thomas came to see the new flat, climbing to the fifth floor in the cramped hallway – and no elevator. Ugh. What a trek. But as the estate agent showed him around the place, he was engulfed by light and the smell of fresh baked bread came from the kitchen. No matter that this was 20 minutes further from work. Thomas was sold.

What’s wrong with this decision case? Well, it looks like Thomas is making his decision to buy an apartment based on criteria that only seem noteworthy at the apartment, not beforehand. Even worse, he ends up being carried away by the fresh smells – surely a trick from the estate agent’s sleeve. I’d venture to say that Thomas hasn’t made an exemplar decision here. What could he have done better?

An old adage works also in decision analysis: Think before you act. In the context of decisions, it refers to thinking about the problem itself first. In decisions, two key parameters largely define your success: the criteria, and the alternatives.

The criteria mean dimensions along which you compare and evaluate the alternatives. For example, for the apartment common ones are size, price, location, and so on. What’s the key is defining those criteria yourself. You don’t have to be constrained by what other people think. Your criteria are anything you care about. For example, one of Thomas’ criteria could be the amount of ambient light in the apartment, if he had thought about it beforehand. Thinking about the criteria before the decision helps to stay on the premeditated path, and not be drawn away by other enticing things. If you’ve given thought to criteria in advance of seeing the alternatives, you’re less likely to focus on salient, but ultimately irrelevant ones (like the fresh smell above). It’s like when you’re going to work: you decide to walk straight there, and don’t go into shops even when you see that shiny new guitar in the window (also, your boss might not value your musical enthusiasm to make it a good idea).

Another thing about criteria: they don’t necessary have to be numeric. Sure, there are benefits to using numerical values, especially when they are objective, like size. But inherently there’s nothing wrong with subjective criteria like a “feel” of an apartment, the comfort of a chair or the taste of a wine. After all, it’s your decision we’re talking about. The only thing that matters is that you can be consistent with the criteria, ie. you can rate equally tasty wines as equal on the taste. This is crucial, because otherwise you might be tempted to reevaluate some criteria to end up with the “best alternative”. The point of evaluation is to determine the best option, not to “prove” the choice ex post facto. 

There’s one other trick that’s useful to remember: thinking about the alternatives. This might sound like an obvious thing, and often it is, too. For example, when buying a flat, most of us tend to spend countless hours on websites and with estate agents, looking at alternatives. However, that’s not exactly what I’m referring to. What’s important as well is conceptual alternative-generating before actual data gathering phase. In concrete terms: thinking about conceptually possible alternatives that you would like. In my case, as we’re thinking about apartments just now, it means the following. I enjoy living with a bike distance to the center, so I’ll mentally think of all neighborhoods that fill that criterion.

The point with this is that your decision quality is driven by the alternatives you’ve come up with. If you don’t find good alternatives, you might consider them nonexistent and fall for the status quo bias. Enlarging the conceptual alternative space will help to see what’s possible. An alternative you didn’t think of won’t get picked.

The major point being: you can improve decisions heavily by structured, reflective thinking. This is an idea that Ralph Keeney, an emeritus professor from Duke University, has championed for decades now (for example, in this paper, or this book). Most decisions are not important enough to require a huge decision analysis trade-off analysis. But thinking is almost free, and has the potential to help a lot.