Insight diagram
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
Insight diagram

This model demonstrates the intertwining relationship between the economic contribution of industrial logging and that of adventure tourism (dominated by mountain biking).

In terms of the revenue from industrial logging at Derby, it is driven by demand of timber and the timber price. However, the forest resources are limited, which will put constraints on the expansion of industrial logging due to regrowth rate and existing forestation.

The tourism can bring economic benefits to Derby from hospitality and selling tickets to local adventure activities. The hospitality income can be determined by the average length of holidaying at Derby and average local pricing for accommodation, food and beverages and related essentials. Tickets sales are largely affected by the similar factors such as average expense per activity and average number of activities that tourists usually choose. Having explained the streams of possible income from the tourism, the key driver for tourism income is the desire or demand to travel. Unlikely logging, tourism is renewable and perpetual. However, logging can be conceived as a major constraint on attracting as many tourists as the economy so desires.

This is because deforestation caused by logging will diminish the natural scenery at Derby and in turn, the tourist operations and attractions based upon natural scenery. Loss of forest resources is likely to make Derby less attractive to visitors.

In short, the tourism and logging both provides economic benefits to Derby but in a competing relationship. However, the sustainability possessed by tourism cannot be rivaled by industrial logging in long term. Logging revenue reveals its advantage at inception of observed time period. Such advantage wears out over the time due to reduction in resources and sluggish regrowth. Eventually. the tourism income turns into the major player. To understand how they co-exist, please simulate the model.