Decision Mechanics, in partnership with Prism Decision Systems, recently developed “Budget Playground”—a school district budget planning iPad app. The app was created as part of a program funded by Cornell University’s New York State Center for Rural Schools.

Budget Playground allows school district planning staff to forecast budgets up to ten years into the future and interactively modify different scenarios to create robust budgets that are acceptable to the school district’s stakeholders.

The iPad app is fed by a REST web service that exposes historical trend data collected and managed by the Center for Rural Schools.

The Center for Rural Schools currently has a web portal that presents the historical data in a form that can be consumed by school administrators. One of the center’s objectives is to decouple the data service from the tools that present that data. This would leverage the existing investment in data by allowing the development of an array of highly focused “client” apps that help school district administrators overcome their most urgent challenges.

Budget Playground represents a trial of this concept.

“The passage of a property tax cap in New York State creates an urgent need for school districts to have new tools to manage the budgeting process,” said Dr John Sipple, Director for the New York State Center for Rural Schools. “The Center for Rural Schools can now offer an intuitive, easy-to-use budget forecasting tool to all school districts in New York State. Budget Playground solves a pressing problem, without being burdened by unnecessary features or complexity.”
For more information contact Dr John Sipple, New York Center for Rural Schools (inquires@nyruralschools.org) or Sean Brady, Prism Decision Systems (Sean.Brady@prismdecision.com).

Potential future developments of budget playground include:

• collaborative planning features to allow groups to work directly on scenarios together
• benchmarking capabilities to allow school districts to compare their budgets to those of similar schools
• expansion of the data service to include, for example, national price indices
• user customization of the reported metrics

Andrew Tait presented at the International Executive Seminar in Political Management and Governance hosted by George Washington University’s Graduate School of Political Management. The theme of the conference was "governing in turbulent times". Andrew's talk covered stakeholder management (including Confrontation Analysis) and the development of political strategy.

The conference was attended by senior politicians, civil servants and representatives from NGOs.

Districts where residents have the highest average educational achievement tend to be the smaller ones. Staying true to the tradition of blogging, we’re stating this without having conducted any research whatsoever. Still, we’re confident in the assertion.

Oh, and did we mention that districts where residents have the lowest average educational achievement tend to be the smaller ones. Yep. That’s right.

Eh? How does that work? Well, it’s a consequence of the higher variability of averages in smaller groups.

In a city like London, the average IQ will be close to the UK national average (probably about 100). Granted, for London, it may be a fraction higher, due to the likelihood that an international city is a draw for talent—but it won’t be much above the average.

However, imagine a picturesque hamlet where all nine residents have average IQs. A successful entrepreneur with a genuis-level IQ (say 160) decides to build his dream house there and move in. Suddenly the average IQ of the hamlet is now 106. If the entrepreneur had moved to London the average IQ of the city would have changed imperceptibly.

Relying on averages alone is misleading. We need also to consider sample size.

Let’s take another example. Imagine there’s a software development project that has three components—a database, a server application and an iPhone application. All three components are essential parts of the overall system.

Each component is assigned to a separate team and all are asked for estimates of how long their projects will take to complete. For the sake of simplicity, we’ll assume that they all say eight weeks—which we’ll interpret as being a 50% chance that the component will be completed within eight weeks. So, there’s a 50% chance that the entire project will be completed within eight weeks, right?

Well, that’s how it would probably be reported by many project managers, but it’s wrong. In fact, there’s only a 12.5% chance that the project will be completed with eight weeks. All three sub-projects have to go well for the project to deliver within eight weeks. So, it’s highly likely that the project will miss its deadline. It’s impossible to say by how much, as the sub-project estimates are single-point estimates, as opposed to (more realistic) distrubtions—but that’s a topic for another time.

Clearly, these are fairly simple examples. But this kind of “average” thinking is going on every day. And, as the importance of data as a decision-making tool grows, sloppy analysis is going to increasingly undermine the value of good data.

Big data is big news. Companies like Amazon, Google and major supermarkets are delivering new services and competitive advantage through analysing their massive datasets. The Economist reports that 30% of Amazon’s sales are through its “you may also like” recommendations.

Organizations everywhere want to make similar use of their own data. Articles and conferences on "predictive analytics" and "data science" are popping up everywhere. Even the New York Times has been promoting careers in statistics as "cool".

But, are we learning the right lessons from the successes of Amazon et al? Should decision scientists be focusing their efforts on helping organizations make sense of the data they have?

Massive datasets are a byproduct of something the showcase "big data" companies do that is, arguably, more important—they crowdsource data in real-time. Both crowdsourcing and real-time data collection are valuable. Together they are dynamite.

Making one small team within the organization responsible for collecting and "cleaning" the "official" data limits the volume of data that can be collected and increases the possibility of bias. Crowdsourcing mitigates those problems—and is cheaper.

The quality of data decays over time. Different industries experience different decay rates, but basing decisions on old data risks missing fundamental changes. Obviously, lagging data is almost useless when responding to a crisis. Real-time data collection means decisions can take the immediate situation into account.

Before you ask how you can draw insights from your existing databases, it may be advantageous to ask how you can build higher quality databases in the first place.

Tools to assist decision-makers are increasingly drawing on existing data and then combining it with the decision-makers’ assumptions and beliefs to predict outcomes and suggest action. These assumptions and beliefs are then often discarded once the decision has been made. However, these are real-time insights from the front-line. Capturing and storing them would allow decision-makers to tap into the current views of their peers—and monitor shifts in these views over time.

In addition to designing decision-making tools to produce insights, we also need to design them to collect insights. The latter activity may be the real innovation.

Privacy

Of course, there are potential privacy implications to be considered in crowdsourcing data. However, collecting data about an organization (as opposed to individuals), in the course of paid employment, and with full disclosure, raises few privacy issues. It is similar to writing and publishing a business report.

With the 2010 (football/soccer) World Cup well underway, it’s only a matter of time before some teams will have to face the heartbreak of leaving the competition due to a missed penalty.

In the “knockout” stages of a football competition each match must produce a winner. If the teams have scored equal number of goals (after additional playing time), the game will be decided on “penalties”. A penalty is a free kick taken from directly in front of the goal, with only the defending goalkeeper between the penalty-taker and the goal.

A professional football player will put the ball in the back of the net in about a quarter of a second. This doesn’t give the goalkeeper enough time to respond, so he must anticipate the likely trajectory of the ball—for example, by diving to the side that the penalty-taker tends to prefer.

Of course, the penalty-taker wants to mislead the goalkeeper. While the penalty-taker may be stronger shooting, say, to the right, doing this every time would make his actions too predictable.

So, the goalkeeper and penalty-taker must optimize their own strategies without knowing the strategy that will be adopted by the other party. This is the type of decision-making activity that can be effectively studied using game theory. The players are attempting to optimize their “payoffs”.

Using game theory we can see that the optimal strategy for both players is to behave unpredictably. They must balance their strengths (e.g. the penalty-takers’ strong side) with the value of potentially misleading the other player. But how does this help your team lift the FIFA World Cup Trophy?

Well…it turns out that most professional football players are also expert economists. A few years ago Ignacio Palacios-Huerta demonstated that top players act in accordance with the theory—i.e. they optimize their penalty-taking strategies. While this may come as a shock to some fans, one can’t argue with the science…

Maybe, once the World Cup is over, we can convince some of the players to pop into their respective finance ministries and offer a bit of advice…