Judgment and decision making with unknown states and outcomes

By Michael Smithson

Michael Smithson
Michael Smithson (biography)

What issues arise for effective judgments, predictions, and decisions when decision makers do not know all the potential starting positions, available alternatives and possible outcomes?

A shorthand term for this collection of possible starting points (also known as prior states), alternatives, and outcomes is “sample space.” Here I elucidate why sample space is important and how judgments and decisions can be influenced when it is incomplete.

Why is sample space important?

When it comes to dealing with unknowns, economists and others traditionally distinguish between “risk” (where probabilities can be assigned to every possible outcome) and “uncertainty” (where the probabilities are vague or unknown). Both of those versions of unknowns assume that decision makers know everything about the sample space.

In the real world, however, people often have to make important decisions when they don’t know everything about the relevant sample space. For example, in a pandemic the virus will mutate in ways and at times that cannot be predicted, but decisions about lock-downs and other measures have to be made based on the known variants. The current sample space for COVID-19 includes the known strains of this virus, especially the “delta” and “omicron” variants. Decisions are made with a background assumption that these are the only variants, even though another variant may already be present or emerging that has not yet been identified.

Problems with current prescriptions for dealing with sample space

Rational decision theories such as expected utility theory require that decisions are based on beliefs about the sample space but they assume that the sample is known completely. And so these theories sweep the problem of how to deal with an incompletely known sample space under the carpet. As a result, there are few prescriptions for how a sample space should be constructed, or how decisions should be made if there is no complete description of the sample space.

Moreover, where there are prescriptions, they don’t always seem to be valid. For instance, economists Karni and Vierø (2013) propose that rational agents faced with an incomplete sample space should adhere to what they call Reverse Bayes.

Suppose that a particular kind of virus has two currently known strains, A and B, and the best estimates of the proportions of cases for people who contract this kind of virus are P(A) and P(B) (where P (B) = 1 – P(A)). Now imagine that a new third strain (C) emerges. Karni and Vierø say that regardless of whatever proportion of the cases C ends up having, the ratio P(A)/P(B) should remain unchanged. For example, suppose that originally P(A) = 0.4 and P(B) = 0.6 (so that P(A)/P(B) = 2/3). If P(C) eventually turns out to be 0.5 then it should also turn out that P(A) = 0.2 and P(B) = 0.3.

Reverse Bayes is attractive, because it says that no matter what unknown elements lurk in the sample space, the probability-ratios among its known elements are fixed. Unfortunately, scholars have come up with counter-examples. For instance, what if getting strain C and recovering protects the person more against strain B than against strain A? Over time, the effects of strain C will increase the P(A)/P(B) ratio.

How people estimate the size of a sample space

In addition to the lack of guidance for how to “rationally” deal with an incomplete sample space, little research has been done on how people actually do deal with this. My colleague Yiyun Shou and I recently investigated how people estimate the size of a sample space based on what they’ve learned and believe about it so far (Smithson and Shou, 2021). It is already established that if people think that an incompletely known sample space is big, they are more likely to be cautious about making decisions based solely on the parts of the sample space they know about and we wanted to explore this further.

Our initial idea was that if people don’t have prior beliefs about the sample space then the larger the number of unique elements in the sample space they’ve seen, the larger they think the sample space will be. This is akin to the models that biologists use when estimating biodiversity in an environment based on the samples of species they have obtained—the greater the number of observed species, the higher the estimate of biodiversity.

Our experiments supported our hypothesis to some extent. When presented with a hypothetical sample of marbles drawn from a large bag of them, the greater the variety of colours in the sample (eg., 4 versus 15 colours), the larger the number of additional new colours people expected to see as more marbles were drawn from the bag. However, this didn’t hold true for the same samples if they were colours of automobiles. Most people in our experiment would have had prior beliefs about how many distinct colours of automobiles there are, and they would infer that having seen 15 of them there would not be many more left to see.

Our experiments also threw up a couple of surprises. We found that not only does a larger number of distinct elements produce larger estimates of the size of the sample space, but greater qualitative diversity among the elements can do this too. For instance, 4 versus 12 unique polygonal shapes of drinks coasters has a smaller impact on estimates of how many unique shapes there are than 4 versus 12 unique shapes consisting of polygonal and animal-figures.

Moreover, the ability to retrieve examples of sample space from memory influences estimates of its size. Thus, 4 versus 12 unique polygonal shapes had a smaller effect than 4 versus 12 unique animal shapes, because most people have a larger supply in their memory of retrievable animals than polygons.

And memory retrieval can even be influenced by priming, which is when exposure to a stimulus subconsciously influences responses to a subsequent stimulus. As Gavanski and Hui (1992) point out, priming memory to retrieve an “incorrect” sample size will render the decision maker more likely to misestimate risks and make worse decisions. In one of our experiments we asked people to estimate how many English words end in “ak”, in one of two conditions:

  • those given a sample of “ak”-ending words that included words ending in “eak” and
  • those whose list did not include such words.

About 78% of the most frequently used English words ending in “ak” end in “eak”. Consequently, those whose sample included the “eak” words gave larger estimates of the number of “ak”-ending words than those whose sample did not. That is, exposure to “eak”-ending words primed people’s memory searches to retrieve a larger sample space of “ak”-ending words, whereas not being exposed to “eak”-ending words primed people to retrieve more restricted sample spaces.

Where next?

Current knowledge about how people make judgments and decisions with incompletely known sample space has barely scratched the surface. Likewise, there are few effective strategies for dealing with sample space ignorance, although there is a scattered literature proposing some guidelines. For instance, Smithson and Ben-Haim (2015) recommend that decision makers should prepare to encounter more surprises than they intuitively expect, and keep sufficiently many options open to avoid premature closure. We also recommend seeking outcomes that are steerable, that have observable rather than hidden consequences, and that do not entail sunk costs, resource depletion, or high transition costs.

Thinking about your own research or practice domains, how commonplace is sample space ignorance? Under what conditions is it most likely to crop up? Are there times when it has led to poor judgments, predictions, or decisions? And do you or others have ways of dealing with incompletely known sample spaces?

Gavanski, I. and Hui, C. (1992). Natural sample spaces and uncertain belief. Journal of Personality and Social Psychology, 63. 5: 766-780.

Karni, E. and Vierø, M. L. (2013). Reverse Bayesianism: A choice-based theory of growing awareness. American Economic Review, 103, 7: 2790-2810.

Smithson, M. and Ben-Haim, Y. (2015). Reasoned decision making without math? Adaptability and robustness in response to surprise. Risk Analysis, 35: 1911-1918.

Smithson, M. and Shou, Y. (2021). How big is (sample) space? Judgment and decision making with unknown states and outcomes. Decision, 8, 4: 237-256.

Biography: Michael Smithson PhD is an Emeritus Professor in the Research School of Psychology at The Australian National University. His primary research interests are in judgment and decision making under ignorance and uncertainty, statistical methods for the social sciences, and applications of fuzzy set theory to the social sciences.

5 thoughts on “Judgment and decision making with unknown states and outcomes”

  1. In my experience a key part of the story of dealing with incompletely known sample spaces involves changing the sample space being explored. As I’ve written previously, it is for example possible to “blackbox” unknown unknowns by focusing on a known vulnerability of an unknown cause.

    This essentially requires an ability to explore the sample space of sample spaces, which is still subject to sample space ignorance. However this time by moving the problem to a higher level we now focus on generalisable strategies for expanding our problem definition, which hopefully leads to more robust solutions, e.g. lateral thinking techniques.

    Recognising what strategy we’re using to explore a sample space is often a good start. With the ak example, recognising that we’re exploring sample space in terms of sound could then lead to testing out what other sounds might be compatible, and hence discovering the eak-sound sample subspace.

    If we’re thinking about policy in terms of characteristics of an incomplete sample space of strains, a good starting point is to think about the robustness of alternative approaches using different sample spaces. How did policy work before PCR allowed differentiation between strains?

    We can often swap a poorly understood sample space for one that is more tractable, e.g. the sample spaces for flexibility, steering options and monitoring options.

    A notable application area for these kinds of ideas is in policy design and the generation of alternatives, e.g. https://doi.org/10.1016/j.ejor.2018.07.054

    • I think that both of your points are helpful. Exploring a sample space of candidate sample spaces is a crucial component of effective meta-cognition, but it’s a neglected topic in cognitive psychology and only recently taken up in philosophy. Some recent philosophical literature discusses conditions under which growing awareness involves refining or expanding one’s sample space versus replacing it with a new and different space. Likewise, your reference to robustness is a key point in the Smithson-Ben-Haim paper (Yakov Ben-Haim invented info-gap theory, a formal and well-developed framework for evaluating robustness of policies, strategies, and models); there we argue that flexibility, steerable options, and ones that permit monitoring are aspects of a sample space that enhance robustness of choices made from it.

      Editor: see Managing innovation dilemmas: Info-gap theory by Yakov Ben-Haim https://i2insights.org/2019/10/08/info-gap-theory/

  2. Dear Michael,
    You are absolutely right. Ignorance of the sampling space is a common occurrence. This creates problems for effective judgments, forecasts and decisions.
    Let me philosophize a little on this topic.
    I think this situation has several ways to develop.

    The first way can be associated with the improvement of methods and tools for the study of incomplete sample space, as well as the actions of people in such a space. It can be assumed that this path will inevitably lead to an increase in the complexity of problems and the complexity of methods and tools. Ultimately, the maximum level of complexity will limit the further effectiveness of research in this direction.

    The second way may be associated with a change in attitude to the formation of the sample space. Simply put, a context will be formed that justifies the completeness of the sample space. I am sure that any problem is a distortion of reality near the horizon of the existing scientific worldview. Therefore, to form such a context, it will be necessary to use a new horizon of worldview.

    Let’s show how it works using an example from your message. You write: «For example, in a pandemic the virus will mutate in ways and at times that cannot be predicted, but decisions about lock-downs and other measures have to be made based on the known variants. The current sample space for COVID-19 includes the known strains of this virus, especially the “delta” and “omicron” variants. Decisions are made with a background assumption that these are the only variants, even though another variant may already be present or emerging that has not yet been identified».

    Indeed, in such a situation, we cannot predict new mutations of the virus and are forced to use information about known strains. But if we expand the horizon of the scientific worldview, the situation will change for the better. For example, knowledge of the mechanism of formation and activation of viruses in the natural environment, as well as the availability of technical means for monitoring this mechanism, allows you to create and justify a complete sampling space about virus strains.

    I think that in order to achieve the completeness of the “sampling space of cognition”, specialists should move in parallel in both directions.
    Mokiy, V. S., & Lukyanova, T. A. (2022). Covid-19: Systems transdisciplinary generalization, technical and technological ideas, and solutions. InformingScience: The International Journal of an Emerging Transdiscipline, 25, 1-21. https://doi.org/10.28945/4893

    • Dear Vladimir,
      You’ve raised some pertinent ideas about the development of methods for dealing with incomplete sample spaces. I’m especially interested in your point about developing a context that justifies sample space completeness. An example that you may already be aware of is the use of tests of stationarity (e.g., unit-root tests in time-series analysis) as a justification for a stationary model of a process. If a process has been deemed stationary then past data about it can be used to predict its future (i.e., we can believe we have a complete sample space of its function form and parameters). However, if the process is believed to be non-stationary then its past becomes less useful (perhaps even irrelevant) for predictions about it.

      • Dear Michael, I completely agree with you. The process of making apple pie is an example of a stationary process. Past data about it can be used to predict its future. But you and I know that not all processes have a recipe for cooking.

        I propose to complicate the initial conditions of the process (depending on the context).
        The first group of processes is the physico-chemical processes in a certain area of the biogeocenosis. These processes, their stages and results, completely depend on the laws of physics and chemistry.
        The second group of processes is the processes of development of objects of the plant and animal world of this biogeocenosis according to biological laws. These processes, their stages and results depend more on the results of the processes of the first group. For example, if the temperature of water and air is constantly decreasing, then kangaroos will grow thicker fur, and platypuses will die out.
        The third group of processes is the development of humans and society according to social laws. These processes do not depend much on the laws of physics and chemistry, as well as on the results of the processes of the second group. A person can build warm houses and learn how to grow food in greenhouses.
        It is obvious that in the direction from the first group to the third group, the processes become less stationary. The freedom to choose solutions and results increases. As a result, the problem will not be the prediction of the future itself, but the effectiveness of risk analysis from its practical implementation. Probably, more or less effective stationarity tests can be developed for each group of processes. For example, it can be argued that human development is moving from the development of simple tools to the fact that developed tools will make it possible to manufacture (spare parts) human organs. As a result, a person will turn into a cyborg, and society into a technical and technological civilization.

        However, a completely different result of development will be in the context of the statement that these groups of processes are elements of a single process of development of planetary nature, each of its biogeocenosis. Generalization of incomplete sample spaces of these processes within the framework of systems transdisciplinary models allows synchronizing their time series. Paradoxically, in this case, the results of the development of groups of less stationary processes will be determined not so much by the objects of these groups and their laws themselves, as by the predetermined results of groups and the laws of more stationary processes.

        Such a biogeosociocenosis context and the method of its formation makes it possible to strengthen the effectiveness of existing methods and means of studying incomplete sample spaces, as well as people’s actions in such a space.

        This example of the formation of an objective context for the study of an incomplete sample space can be transferred to processes of various scales: regional or local. This circumstance can serve as a good reason for organizing cooperation.

        Mokiy, V. S., & Lukyanova, T. A. (2021). Transdisciplinarity: Marginal Direction or Global Approach of Contemporary Science? Informing Science: The International Journal of an Emerging Transdiscipline, Vol.24, pp. 001-018. https://doi.org/10.28945/4752


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