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---
id:
aliases: []
tags:
- authorship/original
- destiny/permanent
- status/draft
- type/daily
title: 2025-11-21
---
# 2025-11-21
## 2025-11-21 10:11
> [!quote] [ELECTRI's Industry Benchmarking Tool - ELECTRI International](https://www.electri.org/research-overview/electris-industry-benchmarking-tool/)
> ### Hours Burned vs. Hours Earned
>
> Definition:
> Comparison between portion of project estimated hours complete
> compared with the actual hours spent on the task.
> This is the labor performance factor
> (needs to reference the labor factor used at bid time for a full comparison).
This is a terribly problematic metric.
If a project went over its material budget
despite standard rigorous oversight
where would you first look,
construction or estimating?
In almost all cases the safer bet is estimating.
Why should labor be different?
Wherever there is budget variance
there is a persistent tendency to blame construction
before estimating.
[[purpose-of-construction-estimating#The Myth of Estimate Accuracy]]
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---
id:
aliases: []
tags:
- authorship/llm
- destiny/fleeting
- status/complete
- topic/estimating
- topic/risk
title: Modeling Bid Prices Under Intrinsic Cost Uncertainty
---
# Modeling Bid Prices Under Intrinsic Cost Uncertainty
> [!warning]
> This text is almost entirely LLM output.
> I don't intend to keep or use any significant portions of it.
Consider a construction project characterized by an intrinsic but unknown final cost $C$.
Prior to project completion, $C$ cannot be observed directly;
instead the estimator possesses only a probability distribution over feasible outcomes.
Let $(\Omega, \mathcal{F}, \mathbb{P})$ be a [probability space](https://en.wikipedia.org/wiki/Probability_space)
describing the estimator's uncertainty,
and define the true cost model as a non-negative random variable
$$
C : \Omega \to [0,\infty)
$$
with [distribution](https://en.wikipedia.org/wiki/Probability_distribution) $\mu_C$.
The distribution $\mu_C$ summarizes all available information at the time of bidding,
including quantities, labor productivity uncertainty,
market conditions, subcontractor pricing variability,
and correlation structures inherent to the estimator's assumptions.
Although the natural mathematical representation of cost is thus a distribution,
procurement mechanisms typically require that each bidder submit a single deterministic price.
Denote this bid by
$$
B \in [0,\infty).
$$
> Read as
> "$B$ is an [element](https://en.wikipedia.org/wiki/Element_(mathematics))
> of the interval from zero to infinity, including zero."
A bid $B$ may be viewed as the output
of a pricing [functional](https://en.wikipedia.org/wiki/Functional_(mathematics))
$$
\Phi : \mathcal{P}([0,\infty)) \to [0,\infty),
$$
mapping a cost distribution $\mu_C$ to a scalar.
Examples of such functionals include:
### 1. Risk-neutral expectation
$$
\Phi(\mu_C) = \mathbb{E}[C],
$$
> Read as "Phi of mu sub C equals the expected value of C."
where $\mathbb{E}[\cdot]$ denotes the [expected value](https://en.wikipedia.org/wiki/Expected_value).
### 2. Risk-adjusted expectation
$$
\Phi(\mu_C) = \mathbb{E}[C] + \lambda\sqrt{\mathrm{Var}[C]},
$$
> Read as "Phi of mu sub C equals the expected value of C plus lambda times the square root of the variance of C."
where $\mathrm{Var}[C]$ is the [variance](https://en.wikipedia.org/wiki/Variance)
and $\lambda>0$ is a risk-loading parameter.
> This mirrors mean--variance pricing common in portfolio theory.
### 3. Quantile-based pricing
$$
\Phi(\mu_C) = Q_p(C),
$$
> Read as "Phi of mu sub C equals the p-quantile of C."
where $Q_p$ is the $p$-[quantile](https://en.wikipedia.org/wiki/Quantile)
of the distribution.
### 4. Utility-maximizing bid
Under a bidder [utility](https://en.wikipedia.org/wiki/Utility) function $U$,
$$
\Phi(\mu_C) = \arg\max_{b\ge0} \; \mathbb{E}[\,U(b - C)\,].
$$
> Read as "Phi of mu sub C equals the argument b greater than or equal to zero
> that maximizes the expected value of U of b minus C."
> [$\arg\max$](https://en.wikipedia.org/wiki/Arg_max) is the value of $b$ that maximizes the expression.
***
The central tension is:
* The ontologically correct representation of project cost prior to execution is a **probability distribution**, whereas
* The procurement mechanism requires a **deterministic scalar**.
The study of such pricing functionals $\Phi$ sits within
[stochastic optimization](https://en.wikipedia.org/wiki/Stochastic_optimization),
[risk measures](https://en.wikipedia.org/wiki/Risk_measure),
and [mechanism design](https://en.wikipedia.org/wiki/Mechanism_design).
Understanding how different choices of $\Phi$
compress and distort the underlying uncertainty $\mu_C$
has direct implications for bidder profitability, competitive strategy,
and how risk is allocated across the construction market.
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## Scope
This note is intended to describe bid process strategy
especially in terms of [[strategy#Auction Theory]],
especially in terms of [[auction-theory]],
which specifically addresses the competitive bid format
typical of construction project award.
@@ -38,3 +38,31 @@ Bidders will accept a lower payout or even a loss
in order to retain employees.
**Backlog Deficit** drives up **Utility of award**
## Factors Brainstorm
This is a list of semi-independent factors
relevant to the contractors decision of if and how to bid.
### Key
* `> [!info]` --- **Fact:** an indisputable truth or term definition
* `> [!important]` --- **Logical Consequence**
* `> [!tip]` --- **Relationship**
* `> [!success]` --- **Desirable Outcome**
* `> [!failure]` --- **Undesirable Outcome**
> [!info]
> The exact cost of a construction project is **uncertain**
> until it is completed.
> [!info]
> Customers request bids as a
> [!tip]
> Spending _more_ time on estimates
> [!tip]
> Estimating _more_ projects leads to _more_ opportunities for award.
> Estimating _fewer_ projects leads to _fewer_ opportunities for award.
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@@ -35,6 +35,28 @@ Because they are not expected to ever look at their work again,
they have no incentive to reveal errors
as they can simply plead ignorance if the error is discovered.
## Incentives
I strongly oppose any project-based incentive structure for estimators.
Such a structure is at odds with the [[purpose-of-construction-estimating]],
and is an example of a [perverse incentive](https://en.wikipedia.org/wiki/Perverse_incentive).
This behavior should be regulated by an accompanying _disincentive_ structure,
but equivalent punishment becomes unfeasible
before monetary rewards become effective.
The absolute worst disincentive an estimator faces is termination,
which, with fully-remote options available, is only an inconvenience.
The timeline to recognize an estimate was deliberately misrepresented
is not much shorter than that of the project.
Such incentives therefore create a [[game-theory|competitive game]]
between the estimator and the employer
where the employer is hopelessly outmatched,
and the estimator's [[strategy#Strictly Dominant Strategy|strictly dominant strategy]]---
resisted only by their moral conviction---
is to abuse the system: to bid fast, lie often, take the bag, and leave.
## Collaboration
Estimation is a profession ripe for productive collaboration.
@@ -80,9 +102,3 @@ to embrace the statistical aspects of our field.
* Be competent with every tool available to you.
* Always assume there is a better way to complete a given task.
## Incentives
I strongly oppose any project-based incentive structure for estimators.
Such a structure is at odds with the [[purpose-of-construction-estimating]],
and is an example of a [perverse incentive](https://en.wikipedia.org/wiki/Perverse_incentive).
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# Game Theory
## Domains
### Auction Theory
[[auction-theory|Auction theory]] is a subset of game theory
<!-- TODO: -->
#### Reverse Auction
In a reverse auction,
bidders compete for the right to _sell_ a product or service.
#### Sealed-Bid Auction
Opposite of a conventional "open" auction,
in a sealed-bid auction,
bid prices are hidden from the bidders.
[[uncertainty#Information]]
<!-- TODO: -->
## Terminology
### "Solved" Games
<!-- TODO: -->
### Symmetry
## Typical Games
### Chicken
| Player A \ Player B | Player B: Back Down | Player B: Hold Firm |
|:------------------- |:-----------------------:|:-----------------------:|
| Player A: Back Down | Tie \ Tie | Lose face \ Gain status |
| Player A: Hold Firm | Gain status \ Lose face | Die \ Die |
### Prisoner's Dilemma
| Prisoner A \ Prisoner B | Prisoner B: Cooperate | Prisoner B: Defect |
|:----------------------- |:---------------------------:|:-----------------------------:|
| Prisoner A: Cooperate | Serve 1 year \ Serve 1 year | Serve 3 years \ Go free |
| Prisoner A: Defect | Go free \ Serve 3 years | Serve 2 years \ Serve 2 years |
### Stag Hunt
| Hunter A \ Hunter B | Hunter B: Hunt Stag | Hunter B: Hunt Hare |
|:------------------- |:--------------------:|:--------------------:|
| Hunter A: Hunt Stag | Eat stag \ Eat stag | Go hungry \ Eat hare |
| Hunter A: Hunt Hare | Eat hare \ Go hungry | Eat hare \ Eat hare |
### Ultimatum Game
### Dictator Game
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## The Myth of Estimate Accuracy
<!-- TODO: -->
This is a dangerous superstition
which encourages unproductive conflict.
@@ -27,6 +25,20 @@ Operations is blamed for failing to meet targets
not intended for the purpose,
...
***
Construction contract bids are inherently risky.
Executives want to bid because construction can be profitable.
Executives hire estimators to mitigate risk of loss.
An estimator's value is determined by their
An estimator's job is to mitigate
Estimators downplay the
## For Each Party
### For the Solicitor
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@@ -14,48 +14,28 @@ title: Strategy
The field of strategy is concerned
with the optimal solutions of problematic scenarios.
## Decision Theory
## Domains
Decision theory concerns
### Decision Theory
* ~~internal problems~~
* ~~no competition~~
* ~~internal optimization~~
[[decision-theory|Decision theory]] concerns choices
made by a single party, based on environmental conditions
## Game Theory
"Player vs. Environment"
Game theory concerns decisions
### Game Theory
[[game-theory|Game theory]] concerns choices
made in competition with other intelligent actors.
Predictions of competitor behavior in bids and market movements
are made with a game-theoretic lens.
"Player vs. Player"
## Terminology
### Dominant Strategy
A dominant strategy is one
that results in the better outcome.
### "Solved" Games
### Strictly Dominant Strategy
<!-- TODO: -->
### Auction Theory
Auction theory is a subset of game theory
<!-- TODO: -->
#### Reverse Auction
In a reverse auction,
bidders compete for the right to _sell_ a product or service.
#### Sealed-Bid Auction
Opposite of a conventional "open" auction,
in a sealed-bid auction,
bid prices are hidden from the bidders.
[[uncertainty#Information]]
<!-- TODO: -->
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* Certain Monetary Equivalent (CME)
* also called Certainty Equivalent
One or more curves referenced in the text are
[Indifference curves](https://en.wikipedia.org/wiki/Indifference_curve)
but not referred to as such.
## Key Takeaways
### Definition of Risk
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@@ -12,3 +12,20 @@ title: Uncommon Syntax
* **i.e.** --- _id est_ ("that is")
* **e.g.** --- _exempli gratia_ ("for example")
## Symbols
* $\therefore$
* $\vdash$ --- turnstile,
denotes logical consequence
* $\vDash$ or $\models$ --- double turnstile,
denotes [semantic](https://en.wikipedia.org/wiki/Semantic "Semantic") consequence
> [!quote] [Double turnstile - Wikipedia](https://en.wikipedia.org/wiki/Double_turnstile)
> read as
> "[entails](https://en.wikipedia.org/wiki/Logical_consequence "Logical consequence")",
> "[models](https://en.wikipedia.org/wiki/Model_theory "Model theory")",
> "is a **semantic consequence** of"
> or "is stronger than".