ForeTell Frequently Asked Questions

What are the hardware and software requirements for ForeTell?
ForeTell is written in Java. ForeTell solutions are self-contained, and come pre-bundled with database and analytics engines, and a Java Runtime Environment (JRE). Including the JRE, solutions require less than 20MB of disk space and can generally run on 256 - 512MB of RAM on virtually any Windows or Linux computer.

What training is needed to use a ForeTell solution?
DecisionPath offers a one-day training course for end-users, which includes numerous hands-on exercises to develop proficiency. This class presupposes familiarity with spreadsheets or comparable software tools, and experience making the relevant type of decision. Typical users are staff analysts or consultants, although executives can easily "drive" the software should they so choose. The ForeTell GUI is straightforward to use, consisting of the customary menus, button controls, output reports, etc.

What competitors to ForeTell should I consider and evaluate?
Competing decision support tools include spreadsheets; decision trees; risk calculators; and simulation engines based on adaptive agents, system dynamics or Monte Carlo technologies. None of these products, however, encompass the breadth of decision elements offered by ForeTell to address the diverse dynamics that shape real world situations; nor do they offer the broad decision- rather than model-focused methodology embodied in ForeTell. In fact, ForeTell solutions can integrate and exploit results from these narrower models, along with information from business intelligence solutions such as data warehouses and executive dashboards.

How do you turn expertise into ForeTell elements?
DecisionPath works with customers and partners to codify decision-making expertise into custom ForeTell solutions. We develop these decision models rapidly and refine them iteratively, based on expert feedback. Most people, including expert decision-makers, don't think about decisions in terms of programming concepts, but rather in terms of performance metrics (or formulas) for assessing outcomes, situational trends and events, rules of thumb (and exceptions), and likely stakeholder responses. DecisionPath consultants apply knowledge acquisition techniques from Artificial Intelligence and object-oriented design methods to draw out and capture this expert knowledge. Moreover, users can inspect textual explanations of this underlying decision "logic" on demand, in order to understand how scenario outcomes were projected. We call this feature "transparency".

Is all information in ForeTell numeric?
No. ForeTell provides a variety of data types to capture decision inputs in an appropriate (and intuitive) form: captures information using a rich variety of data types as appropriate, including free text, symbolic or boolean (true/false) values, lists and tables. Symbols can model categories (e.g., Male vs. Female, Small/Medium/Large), or ranges, such as demographic intervals (e.g., 0-1 years old, 1-10, 11-20,... 80+). You can also annotate any scenario value with "metadata" to document your degree of certainty, comments, ToDos, and sources for that data. These notes help you maintain scenarios and share them with other users.

What if I don't know the information required by a ForeTell solution
Systems that are designed to expect perfect knowledge are generally worthless in the real world. DecisionPath adopts the more pragmatic approach of working with experts to define the kinds of information (and levels of precision) that users are likely to have. Also, ForeTell solutions are often designed around the fact that you may have some level of uncertainty about some data. Another recommended technique is to create an "envelope" around uncertain information. In ForeTell, you do this by constructing scenarios using the minimum and maximum values you expect for a datum, and then exploring the effect of those extremes on the projected decision outcome. ForeTell also provides a Monte Carlo facility for varying multiple data values at the same time using appropriate statistical distributions. This method requires some basic familiarity with statistics.

How flexible are ForeTell solutions?
Typically, ForeTell solutions are built to give end-users considerable flexibility. End-users can quickly populate scenarios with pre-fabricated entities from a library (e.g. reuse entities that model particular cities, organizations, or products), or create and edit new ones with a simple point-and-click GUI. Users can quickly define custom events to test the robustness of strategies or even copy and adapt entire scenarios. Many solutions allow users to modify behaviors as well, for example, by editing tables that specify behavioral rules or using an integrated curve fitting utility on observed data sets. More drastic customizations require involvement by DecisionPath or one of our developer partners. ForeTell solutions are built iteratively and designed to be highly extensible; new attributes, behaviors, and even new types of entities can be added quickly. Of course, such changes, while easy to implement, generally entail re-validating the solution to make sure that it still projects scenarios in a reasonable manner.

How can I trust ForeTell simulations?
This is probably the most important question to ask. The ForeTell methodology prescribes multiple techniques for verifying ForeTell solutions and that help end-users build confidence in them over time. First, ForeTell produces an audit trail when it projects a scenario. This trail explicitly logs every change induced by the simulation engine, including events, trends, causal forces, and actor behaviors. These logs allow developers and end-users to reconstruct what happened, when, and why, to validate the individual pieces of the ForeTell solution. Beyond this "unit-level" testing, we apply system level "sanity" tests. This involves developing scenarios of extreme situations, for which our experts and end-users are likely to have strong intuitions about outcomes. For example, one would expect a retail store that does not stock what its customers expect to perform poorly. We verify that ForeTell projects behaviors in line with these expectations. We then gradually return to more typical scenarios and validate a "return" to normalcy in observed outcomes. The "acid test" is to apply the ForeTell solution to historical situations. We construct a scenario that represents the time at which a decision is made and an evolution that reflects what actually took place. If ForeTell solution projects an outcome that matches actual historical results, it acquires important credibility.

What happens to intuition and "gut instinct"?
Decision-makers often feel that they are being paid for their experience and instincts, and may be reluctant to use ForeTell solutions. ForeTell is intended to complement rather than replace these valuable qualities. Leaders' intuitions drive many of the inputs for ForeTell scenarios. They also drive what scenarios should be considered: which entities are relevant and how they are likely to behave, what events might happen, what trends are perceived, etc. Using ForeTell simply ensures a higher level of detail and thoroughness than is possible from individual mental models and gut instinct. ForeTell solutions provide independent justification for intuitive decisions. They also provide an explicit audit trail that demonstrates due diligence, a systematic and repeatable process (part of compliance with Sarbannes-Oxley). In the case of discrepancies, ForeTell provides a platform for exploring why the model differs from instinct and for figuring out how to adjust one or the other.