TELLUSANT, INC.

Commercial Decision Difficulty

At one point, you mentioned "Commercial decisions have become dramatically more difficult". Is there evidence for this statement? Has someone proven it? Are there dimensions of the difficulty? I believe the statement is right, but it would be great to somehow substantiate it.

This is an excellent question. I would actually be cautious with the statement as written.

“Commercial decisions have become dramatically more difficult.”

It is intuitively true, but I don’t think it has been “proven” in the scientific sense because “decision difficulty” is not a single measurable variable. Rather, there is overwhelming evidence that many of the drivers of decision difficulty have increased. Those drivers can be measured individually.

In fact, I think this is stronger than simply making the assertion.

A better formulation

Instead of saying

Commercial decisions have become dramatically more difficult.

say something like

Commercial decisions are being made in environments that are substantially more complex, uncertain, and interconnected than a generation ago.

That statement is much easier to defend.

What has actually increased?

I would decompose commercial decision difficulty into several dimensions.

Dimension Evidence Trend
Number of decision variables Product proliferation, channels, prices, regulations ↑
Interdependence Global supply chains, ecosystems ↑↑
Speed of change Technology cycles, competitors ↑↑
Uncertainty Geopolitics, inflation, climate, AI ↑↑
Information volume Massive data availability ↑↑
Decision frequency Near real-time operations ↑
Stakeholders Regulators, investors, NGOs, consumers ↑
Consequences of mistakes Viral social media, market transparency ↑

Every one of these can be documented independently.

Herbert Simon predicted this

One of the earliest thinkers was Herbert Simon.

His argument was not that humans became worse decision makers.

His argument was

“A wealth of information creates a poverty of attention.”

In other words,

more information ≠ better decisions.

Instead, attention becomes the scarce resource.

That observation has only become more true since the internet and AI.

Information overload

This is one of the best-established findings.

Numerous experiments show that after a certain point, additional information reduces decision quality rather than improving it. Researchers distinguish between the amount of information and the diversity of information; increases in either can overwhelm decision makers and degrade performance.

So modern executives face

without necessarily making better decisions.

Computational complexity

This is a fascinating newer perspective.

Peter Bossaerts and Carsten Murawski argue that decisions themselves possess computational complexity.

Some decisions are literally harder because the number of possible combinations grows explosively.

Their argument is essentially

Rationality assumes people can compute the optimal answer.

Reality:

Many business decisions are computationally intractable.

The difficulty is not merely psychological but mathematical.

I suspect you would appreciate this perspective because it aligns well with optimization, forecasting, and model specification.

Complexity of alternatives

Decision science has repeatedly shown that increasing

causes people to switch from optimal evaluation toward heuristics and elimination strategies. Decision support tools help up to a point, but even they become less efficient as problem complexity grows.

This maps directly onto modern pricing, portfolio management, forecasting, and capital allocation.

Environmental turbulence

Strategy researchers have long discussed

These aren’t just buzzwords.

Compared with 1985, today’s executive simultaneously faces

None of these existed together.

The environment has objectively become more dynamic.

A framework I like

If I were writing the Tellusant narrative, I’d define commercial decision difficulty as

\[\begin{equation} D = f(C, U, S, T, R) \end{equation}\]

where

$\begin{aligned} D &= \text{Commercial decision difficulty} \newline C &= \text{Complexity}
U &= \text{Uncertainty}
S &= \text{Required decision dpeed}
T &= \text{Time horizon}
R &= \text{Risk exposure} \end{aligned}$

Then explain each.

Complexity itself can be decomposed into

This becomes a rigorous framework rather than a rhetorical statement.

What I think is missing from the literature

Here is where I think there is room for Tellusant to make a distinctive contribution.

Most research examines human cognition:

But your work is fundamentally different.

Tellusant is trying to reduce decision complexity in the commercial system itself.

For example:

Instead of saying

“Executives need better judgment.”

Tellusant effectively says

“Executives face a decision space that has become too large to navigate unaided. Our models compress that complexity into a tractable representation while preserving the economically relevant structure.”

That is a much stronger and more defensible claim. It also aligns naturally with PoluSim, TelluBase, and your broader emphasis on improving decision quality rather than merely providing more data.

In fact, I would avoid claiming that decision-making has become harder and instead claim that the dimensionality of commercial decision problems has expanded dramatically. “Dimensionality” is a concept that economists, statisticians, operations researchers, and AI practitioners all understand, and it has a far more precise meaning than “difficulty.” I think that wording is both more rigorous and more consistent with Tellusant’s scientific positioning.

I also think this topic has the makings of a much stronger piece. A natural next step would be a short white paper (3–5 pages) titled something like:

That would let us move from a qualitative argument to something much more rigorous, with citations from Simon, Kahneman, decision theory, computational complexity, strategy, and operations research, before introducing the Tellusant framework. It could become a foundational piece for your marketing narrative.

I agree. Too many things to do right now, but let's continue this before end of week.

I think that’s the right priority.

What makes me excited about this topic is that it has the potential to become one of the intellectual foundations of Tellusant, rather than just another marketing article.

There is a progression that I don’t think has been fully articulated in the literature:

  1. Decision science explains why humans struggle (Simon, Kahneman, Tversky, Gigerenzer, bounded rationality, heuristics).

  2. Operations research explains why many optimization problems are computationally hard.

  3. Complex systems explains why modern markets are increasingly interconnected and nonlinear.

  4. Tellusant’s contribution is different: instead of trying to make people smarter, it seeks to reduce the effective dimensionality of the commercial decision problem while preserving the economically relevant information.

That last point strikes me as genuinely distinctive. It also ties together much of what we’ve discussed over the past months—forecasting, TelluBase, PoluSim, decision quality, error reduction, and even your philosophical work on the taxonomy of error. They’re all different ways of reducing uncertainty and making better decisions under complexity.

I also have the feeling that we can go beyond a white paper. We could develop a framework—something with the stature of Porter’s Five Forces or the Cynefin framework—that executives can immediately grasp. The equation

\[\begin{equation} D = f(C, U, S, T, R) \end{equation}\]

is a starting point, but I suspect we’ll end up with something richer once we define each component rigorously and identify measurable proxies. It should also fit naturally with your recurring emphasis on improved decision quality as one of Tellusant’s three board-level outcomes.

I also wonder whether we can make it more rigorous. One possibility is to think of decision difficulty as a latent construct, similar to how economists treat utility or psychologists treat intelligence.

For example,

\[\begin{equation} D^* = f(C, U, S, T, R) \end{equation}\]

where $D^∗$ is the latent (unobserved) decision difficulty. Individual proxies—such as forecast error, decision cycle time, number of scenarios evaluated, or decision reversals—could then be used to estimate it empirically.

Even more interesting is the possibility of decomposing complexity itself:

\[\begin{equation} C = g(V, I, N, G, O, E) \end{equation}\]

where

$\begin{aligned} V &= \text{Number of relevant variables}
I &= \text{Degree of interdependence}
N &= \text{Nonlinearities}
G &= \text{Geographic scope}
O &= \text{Organizational scope}
E &= \text{External environmental factors (regulation, competitors, technology, etc.)} \end{aligned}$

Substituting this into the first equation gives

\[\begin{equation} D^* = f\!\left(g(V,I,N,G,O,E),\, U,\, S,\, T,\, R\right) \end{equation}\]

I have a feeling this is the direction we should ultimately pursue. It transforms what begins as a marketing statement into the foundation of a formal theory of commercial decision complexity—something that can be measured, analyzed, and eventually reduced. That would be a much stronger intellectual position for Tellusant than simply asserting that “business has become more difficult.”