The garbage can model and investment decisions

Investors within organizations often face the garbage can model of decision-making. Decisions are made in a chaotic environment where goals are unclear, and participation in the process is fluid. There is often organized anarchy. Problems arise and need attention. They could be random. Solutions may or may not exist when the problem appears. The participants that may help with the decision may shift, and the choices that need to be made, when and how much, are often unclear.

Within this model, resolution is needed quickly but is not always clear. Oversight is difficult because it is not always clear who is in charge of the decision. There is often an issue with flight. Decisions are delayed or abandoned as priorities shift. The garbage can model differs from rational decision-making, yet it more closely reflects reality. 

In a chaotic environment, decision-making is not optimal; anarchy is present. Hence, investors have to break the cycle of decision-making and focus on process. One simple approach is to use models that rationalize decisions, but sometimes choices are unique and require decisions outside a model. Organizations must become process-oriented to avoid these issues. 

So what is your process? Does the organization follow it? Is there clear responsibility for who has to decide? Are preferences clear? There should be a linear sequence of actions that lead to a decision, not all information and choices coming at the same time.  

Stories are important models yet flawed

“Partial knowledge is more often victorious than full knowledge: it conceives things that are simpler than they are and therefore makes its opinion easier to grasp and more persuasive."    - Nietzche 

"History will justify anything. It teaches precisely nothing, for it contains everything and furnishes examples of everything."

- Paul Valery 

Storytellers are model builders. There is a need to describe the causal basis of experiences. A stream of events needs some causal explanation or sense-making that is realistic and comprehensible. A story will use stylized facts to support an argument, but stories are not the same as models. It is a process of causal inference that is not always based on empirical testing from large samples of data. 

Stories are especially important when data are limited or uncertainty is high. In environments of radical uncertainty or complexity, a story or narrative is a useful tool for describing the environment and making sense of causal relationships. 

Organizations use stories to help explain their actions. The story will serve as a model for how an organization behaves. Nevertheless, these stories focus on certain mythical themes: rationality, hierarchy, individual leader significance, and historical efficiency.

The story can be flawed, so investors must question the underlying assumptions behind any story, no different from how model assumptions are reviewed and tested. Stories should be reviewed through a rigorous process to avoid flawed logic based on partial knowledge.  

The two components of investor intelligence

We can think of intelligence in two components. Both are needed to be an effective trader or investor. First, an intelligent person needs to effectively adapt to an environment. Managing the environment requires resources, so an individual must know how to use them. It requires knowledge about the world and the ability to make decisions. Second, people need to interpret experiences within an environment. The skill to provide meaning and learn. Experiential learning links these two forms of intelligence. 

Intelligence is about adaptation, which may involve low intellect, where success is obtained through limited effort and causal understanding, or high intellect, which is associated with a need to understand the causal structure.

This leads to three forms of intellectual adaptation: rules and heuristics. The second is harder because rules may not work, especially in dynamic environments. The second is imitation, where one actor follows another’s success. The third mechanism is selection, which reproduces attributes associated with prior success and eliminates those associated with failure. Yet, controlling or managing the investment environment is difficult.

Success in controlling an environment is limited because history is complex and stochastically uncertain, and outcomes depend on the sequence of choices that lead to a specific action. Success depends on the sample of experiences and the sample of outcomes. 

Intelligent investing is more than applying models; it is an attempt to control a complex environment that requires understanding, action, and learning.

The ambiguities of experience - impacts decision-making

 

James March is one of the great professors of organizational management, yet finance and investment professionals rarely discuss or even consider much of his work. This is odd because organizations, not individuals, make so many investment decisions. Organizations, through investment committees, are a key driver of all institutional action. Organizations manage risk and oversee individual managers.

In his older book, The Ambiguities of Experience, March focuses on the paradox that experience "may be the best teacher" but "is not a particularly good teacher". Organizations are driven by experience, yet the links between action, decision, outcome, and learning are often unclear. The problem is that there are many ways to interpret the link between action and outcome. We have discussed this before in our work on “wicked” environments. 

Experience can be noisy because many links can attach to any action. We cannot always say that our action caused an outcome. Our experiences are also selective. We will often focus on the decisions we made that turned out to be correct and dismiss our incorrect actions. Similarly, success and failure can be misleading. If something worked well, we will likely repeat it. If some action fails, we are likely to stop, yet this stopping rule may not be correct when there are probabilistic outcomes. Additionally, the world changes, so the lessons in the past may not apply, and our aspirations of what is successful also change. There is ambiguity because the environment is complex, noisy, endogenous, constructed, and miserly from small samples.

Experience may not contain the correct lessons for investors or traders. Ambiguity remains about what happened, why it happened, whether it was good or bad, and whether our actions affected the results. Superstitious learning assumes that because some action of ours is followed by a result, the takeaway is that we did that. Is experience unreliable? No, but it should be tempered. 

The key issue for investors and any asset management organization is that causal inference is difficult. The problem permeates all organizations, and inference is not an issue that is left to science. Organizations, like individuals, have behavioral biases tied to group action and decisions. Organizations must explore to learn, then convert that learning into exploitation or action.