vault backup: 2026-01-09 14:57:19

2026-01-08.md

@@ -12,7 +12,14 @@ tags:
 
 ## 2026-01-08 13:33
 
-### 2100 Crystal Drive
+### 2100 Crystal Drive Takeoff Review
+
+#### Generators
+
+> [!failure]
+> Generators were erroneously broken down in switchgear.
+
+[[switchgear-takeoff#Generator|Generator]]
 
 #### Switchgear
 
@@ -23,17 +30,17 @@ Includes
 * submetering
 * coordination study
 
-#### Generators
-
-> [!failure]
-> Generators were erroneously broken down in switchgear.
-
-[[switchgear-takeoff#Generator|Generator]]
-
 #### Composite Cleanup
 
 1 day per week
 
+$$
+\text{Average Weeks Per Month} = 
+$$
+
+> It occurs to me I don't know all the rules of our calendar
+> [Gregorian calendar - Wikipedia](https://en.wikipedia.org/wiki/Gregorian_calendar)
+
 #### Temp Power
 
 $10,000 per [[heavy-equipment#Swing Stage Scaffolding|swing stage]]

2026-01-09.md

@@ -0,0 +1,86 @@
+---
+id:
+aliases: []
+title: "2026-01-09"
+tags:
+  - authorship/original
+  - destiny/permanent
+  - status/draft
+  - type/daily
+---
+# 2026-01-09
+
+## 2026-01-09 10:00
+
+### 2100 Crystal Drive
+
+`#600 ? WHITE ?` had incorrect sort codes.
+
+#### Lighting Control
+
+I took of lighting per plans (E510)
+in spite of proposal stating "local control".
+Will have to be changed.
+
+#### Labor Factor
+
+Fire Alarm
+Switchgear
+Feeders
+Subfeeds
+Corridors
+Amenity
+Retail
+Units
+
+#### Fixtures
+
+> [!failure]
+> Several fixtures were erroneously based on NM cable.
+
+I built some fixtures with \#12/3 in areas with emergency lighting
+to be an unswitched hot.
+Joel is having me change them to \#12/2.
+
+#### Units
+
+> [!failure]
+> One unit type typical was missing `Area` quantities.
+> Another had no takeoff.
+
+#### Labor Plan
+
+$$
+\mathbb{E}\left[\frac{\text{Hours Per Unit}}{\text{Openings Per Unit}}\right] \approx .75~\text{Hours Per Opening}
+$$
+
+High Rise .110--.120 hr/sqft
+
+## 2026-01-09 12:00
+
+$$
+\frac{146097}{400} = 365.2425~\text{Days Per Year}
+\frac{20871}{400} = 52.1775~\text{Weeks Per Year}
+\frac{6957}{1600} = 4.348125~\text{Weeks Per Month}
+$$
+
+$$
+\frac{365.2425~\text{Days Per Year}}{7~\text{Days Per Week}}
+= 52.1775~\text{Weeks Per Year}
+$$
+
+$$
+\frac{52.1775~\text{Weeks Per Year}}{12~\text{Months Per Year}}
+= 4.348125~\text{Weeks Per Month}
+$$
+
+## 2026-01-09 14:45
+
+[[bid-price-modeling]]
+
+Suppose a true cost model,
+accounting for all relevant information available at time $t$.
+
+$C(t)$ returns a distribution whose [scale](https://en.wikipedia.org/wiki/Scale_parameter)
+decreases with $t$, and $C(0)$ maps to a single value.
+$t>0$ is time until the final payment.

bid-price-modeling.md

@@ -12,8 +12,15 @@ title: Modeling Bid Prices Under Intrinsic Cost Uncertainty
 ---
 # Modeling Bid Prices Under Intrinsic Cost Uncertainty
 
+The cost of a construction project is inherently uncertain until it is completed,
+therefore the most accurate model of cost is a distribution of possible costs.
+Customers request bids as a single cost, however,
+so a contractor must determine some function
+to convert from the true cost model to a single bid price.
+
 > [!warning]
-> This text is almost entirely LLM output.
+> From this point forward,
+> 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 $C$.
@@ -28,9 +35,12 @@ $$
 C : \Omega \to [0,\infty)
 $$
 
+> Read as
+> "C is a function from omega to the interval from zero to infinity, including zero."
+
 with [distribution](https://en.wikipedia.org/wiki/Probability_distribution) $\mu_C$.
 
-The distribution $\mu_C$ summarizes all available information at the time of bidding,
+The distribution $\mu_C$ accounts all available information at the time of bid,
 including quantities, labor productivity uncertainty,
 market conditions, subcontractor pricing variability,
 and correlation structures inherent to the estimator's assumptions.
@@ -59,7 +69,7 @@ mapping a cost distribution $\mu_C$ to a **scalar** (a single value).
 
 Examples of such functionals include:
 
-### 1. Risk-neutral expectation
+## 1. Risk-neutral expectation
 
 $$
 \Phi(\mu_C) = \mathbb{E}[C],
@@ -69,7 +79,7 @@ $$
 
 where $\mathbb{E}[\cdot]$ denotes the [expected value](https://en.wikipedia.org/wiki/Expected_value).
 
-### 2. Risk-adjusted expectation
+## 2. Risk-adjusted expectation
 
 $$
 \Phi(\mu_C) = \mathbb{E}[C] + \lambda\sqrt{\mathrm{Var}[C]},
@@ -80,9 +90,9 @@ $$
 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.
+> This mirrors mean-variance pricing common in portfolio theory.
 
-### 3. Quantile-based pricing
+## 3. Quantile-based pricing
 
 $$
 \Phi(\mu_C) = Q_p(C),
@@ -93,7 +103,7 @@ $$
 where $Q_p$ is the $p$-[quantile](https://en.wikipedia.org/wiki/Quantile)
 of the distribution.
 
-### 4. Utility-maximizing bid
+## 4. Utility-maximizing bid
 
 Under a bidder [utility](https://en.wikipedia.org/wiki/Utility) function $U$,
 
@@ -106,7 +116,7 @@ $$
 
 > [$\arg\max$](https://en.wikipedia.org/wiki/Arg_max) is the value of $b$ that maximizes the expression.
 
-***
+## Conclusion
 
 The central tension is: