The purpose of the Brightline simulation is to create a simplified, accurate model of a market economy in which businesses compete against each other for a fixed pool of consumers. Brightline currently models five companies that can be customized by the user. Additionally, the shareowners (owners) of one of the five companies are given the potential to become actively involved in running the company, should the company's performance fall below their expectations. The company assigned to have potentially active shareholders will be called the "Focus" company.

Scope

The Brightline simulation encompasses five companies from any given industry competing against each other for a fixed pool of consumers. All parameters in the simulation are created to operate on a random basis. The user then adds certain "influences" to the basic random nature of the program to entice the companies and consumers to act in a manner consistent with a certain market. The time span for the market competition can be determined by the user, but ten to fifteen years is suggested for purposes of simulating the long term investing typically used by institutional investors.

The five companies must compete in the presence of potential government regulation and shareowner "moderation" of their externalization practices. The fixed pool of consumers represents the total market for the industry. The fraction of the total customer pool using a given company as its supplier therefore represents that company's market share in the industry. Highlights of the simulation are described below:

Principles of Customer Movement: Consumers, or customers, are directly correlated to earnings in Brightline. Companies therefore compete to gain and retain the largest customer base possible. Consumers are attracted to companies with the lowest price product, also known as the best value supplier. Companies can only make their product more attractive by increasing their externalization and, therefore, lowering the price of their product. In the Brightline simulation, customers will remain with their current supplier until they notice a lower price supplier. Once "seen", through a survey of the market, they will consider moving to the new supplier according to predefined Brand Loyalty levels.

Government and Shareowner Intervention: When a company is externalizing beyond the government-defined legal limit, the government can impose a fine and bring the company's externalization back down below the legal limit for a set amount of time. Fining therefore makes the illegally operating company less competitive for a set amount of time. Shareholders of the Focus company have the potential to become active managers in their company, should two conditions be met: 1) their company's market share must fall below a specified level, and 2) the company must be externalizing beyond the legal limit. Once activated, the Focus company shareholders prevent their company from externalizing over the legal limit for the remainder of the simulation.

Statistical Analysis of Brightline

A statistical analysis of the Brightline program was performed to ensure the integrity and non-biased performance of the program. To demonstrate the validity of simulation results in Brightline, we began by analyzing the functioning of all input parameters for the simulation independently and in relation to each other. We began by demonstrating that, with all companies set to function according to the same rules, the basic random movement of consumers does function properly. We then determined that with each company set, one at a time, to have a differential market advantage or disadvantage, the other four companies would win randomly. Such tests were conducted for each parameter that can be set by the user.

Extensive efforts were taken to ensure that the Focus company was modeled in an impartial, unbiased way with the exception of the intended functions of the active shareowners. Indeed, testing demonstrated that the only additional inputs into the Focus company are active shareowner effects.

We have begun testing to determine the potential advantages of Eco-friendly companies and of active shareholder intervention in long-term investment demonstrated by most institutional investors. Analyses presented in this book, while still in their early stages, clearly demonstrate these proposed advantages. We have seen that, in the short-term, the most aggressive, externalizing and Eco-unfriendly companies gain a market advantage. However, we can reliably demonstrate that, with a 12-year investment horizon, a discount rate of 6%, and all companies operating with the same management strategy, the Focus company "wins" the market 17 out of 20 times (p<0.01).

Oil Industry Analysis

For our Brightline analysis of the oil industry, we were fortunate to have the expert input of Dr. Martin Whittaker, principal author of the 2000 Global Integrated Oil and Gas Survey for Innovest. Dr. Whittaker took the lead role in defining how to accurately represent the oil industry in the Brightline parameters. With Dr. Whittaker's assistance, we were able to pit British Petroleum, ExxonMobil, Total Fina, Imperial Oil, and Occidental against each other for 12 years of simulated industry competition. Detailed and described below are explanations of the Brightline parameters used as inputs in this simulation.

Interest rate used for discounting: Because of the 12-year time span of the simulation, we needed to provide the asset managers with a consistent language with which to evaluate and justify alternatives. We applied a 6% discount rate to all earnings to account for inflation.

Run time: In this analysis, we sought to simulate the long-term investing of most institutional investors. We chose 12 years in order to represent investments longer then the average tenure of senior executives, and yet be short enough to be realistic in terms of projection.

(Cycle length): Although not a parameter to be set in Brightline, it merits description here. Each year in a Brightline simulation is broken down into 24 two-week cycles (for a total of 288 cycles). In the simulation, customers, companies and shareholders are given the option to make decisions and changes in their business once every cycle. This two-week time span for a cycle is intended to be short enough to allow consumers and customers to react quickly to a changing environment, yet be long enough to stabilize the potentially frenetic movement of customers allowed by the speed of computer modeling.

Customer Brand Loyalty: In every industry there is a notion of loyalty to the brand, or reluctance to change suppliers for whatever reasons. In the oil industry, there are two main types of customers: industrial customers (large corporations) and customers who pay at the pump. Brand loyalty at the pump tends to be low, as customers can willingly and easily, say, drive a block further to buy gas at 2 cents less per gallon. We chose a 30% brand loyalty for this portion of the market. Industrial customers are more reluctant or unable to change suppliers than individuals. We estimated 80% brand loyalty for them. We then estimated that 2/3 of industry revenues are ultimately due to individuals and 1/3 to industrial customers. Using these numbers to average the loyalty, we arrived at a 60% overall brand loyalty which we used in the simulation.

Shareholder Reactivity: Shareholder reactivity determines the likelihood that shareholders of the Focus company will become "angry", and thus actively involved in the management of a company, should certain conditions in the Shareholder Anger Mode (described below) be met. This parameter was not used in these analyses because, in the baseline simulation, no shareholders were allowed to become actively involved, and in the Focus company simulations, shareholders were set to be active from the start.

Government Vigilance: Different industries are more or less scrutinized by regulators, public interest groups, and the general public eye. The Government Vigilance parameter is intended to represent this scrutiny that keeps companies in an industry from externalizing their costs beyond reason. In Brightline, this parameter functions by determining the probability that the government will fine a company if it has exceeded the legal limit. We decided that a 60% probability of fining, in each cycle, would best represent the world environment within which the oil industry operates.

Vigilance Mode: This parameter determines how the government selects a company to fine, should it choose to fine one in a given cycle. We have used what we call the "weighted-exceed mode". In this mode, one company among those that have exceeded the Legal Externalization Limit (described below) is selected to be fined at a probability proportional to its externalization over the legal limit in that cycle. Using this setting, the company that is operating most illegally has the highest probability of being fined, but all illegally operating companies are vulnerable.

Brightline: Brightline is a parameter which represents the "operating freedom" that a new industry enjoys before they externalize their costs on the world to an excessive level. It is in essence a period of time when companies are operating within reasonable bounds on their own accord, and are thus not moderated by the government or shareholders. In the Brightline simulator, this parameter functions by allowing a set "pool" of total externalization that is allowed to an industry before the government is activated to control externalization via fining. Because the oil industry is quite mature and well monitored, we did not use this parameter in our simulation.

Legal Externalization Limit: This parameter is used to set the total "units" of externalization that are allowed for a given company before they risk fining. Although it will be described in more detail below (in Management Aggressiveness), suffice it to say that a unit of externalization is equal to a unit of competitiveness. The more a company externalizes its costs, the lower the price of its product. However, externalizing costs are placed on society (for example, higher toxic gas emissions due to outdated equipment), and society will only absorb a finite amount of "waste". This parameter defines that limit which society (the government) determines is allowable.

Cycle shareowners may become active: This parameter allows us to keep shareowners of the Focus company from becoming active shareholders for a specified amount of time. For our baseline analysis, where no company was allowed to have active shareholders, we set this to the 289th cycle (after the simulation had already ended), to prevent shareholders from activating. For our analyses with different Focus companies, we set all shareowners to be active from the beginning, so as to show the current potential of active shareholders.

Number of votes needed for shareowners to become active: Brightline assumes that there are 5 significant shareowners in each company. If the company is a Focus company, their shareholders have the potential to become active, should the performance of a company fall below defined standards (established in Shareholder Anger Mode, below). This parameter defines the number of votes, out of 5, needed to activate the shareowners and force the company into compliance with the Legal Externalization Limit. This parameter was not needed in our analyses.

Supplier Selection Mode: This parameter determines how customers potentially choose a new supplier (company) for their services. We used what we call a "random mode". Using this mode, in each cycle, customers will look at one other company, chosen at random, as a potential supplier. If the price of their product is lower, and their Brand Loyalty allows, they will move to the new company. If the other company's price is higher, they will stay with their current company. Consumers therefore do not survey the whole market place every two weeks for a new supplier. Use of this mode in particular reflects Brightline's roots as an agent-based, complexity theory model. A former member of the SWARM programming team at the Santa Fe Institute created the core simulation classes used in Brightline. Experimentation with SWARM which suggests this approach to be more realistic than a completely random mode.

Shareholder Anger Mode: This parameter sets the conditions that make the shareholders angry, thus involving them in the running of their company. We used a mode that causes the shareholders to become angry if their company's share price falls below ½ of the market leader's share price.

Penalty Hold Time: This parameter determines how long the effect of a fine imposed on a company will last (fining will be described in more detail below in Constraint Level). We estimated that in the oil industry, 6 months would best represent the real world. This estimation is based on observations that results even as tragic as the Exxon Valdez incident are real, but not long lasting in the public market.

Constraint Mode: This parameter gives a number of options for how a company is penalized when they are fined. We selected a mode (mode 4) that holds the company's externalization at a pre-defined level (to be defined in Constraint Level, below).

Constraint Level: This parameter defines the externalization limit at which a company is held for the duration of the Penalty Hold Time if they are fined. This parameter is only used if Constraint Mode is set to 4. We estimated that in the oil industry, a 25% reduction in competitiveness would be most appropriate. This estimate is based on observations that companies can be hit fairly hard in the short term for certain acts. Take for example Shell attempting to dump the Brent Spar platform in the North Sea. Although this is not illegal, Shell lost 20% of its market share in Germany in one week. We set this parameter to 3, or 25% below the legal limit of 4.

Company Management Aggressiveness: This parameter is the crux of the oil industry analysis, and the data for which we are most indebted to Innovest. This parameter sets the willingness of the management of each of the 5 companies to externalize their costs. Recall that externalizing costs makes a company more competitive in price, but unloads the company's internal burdens on society. Therefore, the more aggressive a company, the more they unload their costs on society. We used the Innovest EcoValue 21 scores from their 2000 Global Integrated Oil and Gas Survey. We picked our 5 companies based on their international presence and their broad range of EcoValue scores. Dr. Whittaker was kind enough to provide us with preliminary scores before the actual publication was released. EcoValue scores go from best to worst as follows: AAA, AA, A, BBB, BB, B, CCC. BP received an AAA, ExxonMobil an A, Total Fina a BBB, Imperial a B, and Occidental a CCC. To these scores we attached the following Management Aggressiveness numbers (significance to be explained later). BP was a 20, ExxonMobil a 16, Total Fina a 14, Imperial a 12, and Occidental an 8. These numbers determine the likelihood that a company will increase their externalization in a given cycle. A 20 means that there is a 1 in 20 chance in each cycle that a company will increase externalization. An 8 means that there is a 1 in 8 chance (more likely) that they will increase, and so on in between.

The results presented in the main text of the book use the Brightline settings we have described above. The Brightline simulator and the parameter file used for the baseline Oil Industry analysis can be seen at http://www.ragm.com/brightline/index.html. The version currently available online is slightly older than the one we have used here. However, Brightline is a work-in-progress and new versions will be updated regularly. Indeed, we encourage any interested parties to use Brightline and give us feedback on the program. We consider Brightline to be an ongoing project and value users suggestions.

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