Much of the effort invested in building energy efficiency and conservation is focused on analyzing or simulating individual physical systems within a building, to help designers understand, e.g., what savings could result by replacing standard lights with high-efficiency fluorescents, or by using light-colored paint on a building’s roof. Typical approaches combine simulating the actual physical properties of building systems, and statistical data based on historical usage. However, the complex interactions between building systems and the environment make accurate estimations difficult.

The complexity of this problem increases dramatically when occupant behavior is included. Consider a simple hypothetical example: a building consumes $1M per year in electricity for lighting. Analysis might show that, given current use patterns, installing high-efficiency lighting would cost $1M and result in 50% electricity savings – $500,000 per year – which would lead to break-even in two years. However, suppose that the building owner invests in a campaign to increase awareness, leading to a 25% reduction in how much lighting is used by occupants, or an annual electricity cost of $750,000. The same high-efficiency lighting would now only save $375,000 per year, and would thus take nearly three years to reach break-even.

Even more complex interactions take place when one starts to consider all building systems – such as heating and cooling, appliances and data networks – and other aspects of occupant behavior which impact demand and usage patterns. For instance, improving climate control might encourage occupants to spend more time inside the building, leading to an increase in energy consumption.  This is sometimes referred to as the ”rebound effect“.

This type of emergent behavior is a hallmark of complex systems, systems whose overall behavior is determined in sometimes unpredictable ways by the elements of the system interacting with one another and with the environment. We can see examples of human-made complex systems all around us: traffic jams, stock market fluctuations and even sports team performance cannot be predicted even when we know well how each individual is behaving within the system. Similar principles are at work in natural systems, including for examples the flocking behavior of birds, the schooling behavior of fish, of the ability of social insects such as termites to build incredibly complex structures – without the benefit of blueprints and architects.

Traditional analytical techniques are ill-equipped to manage complex systems, whose behavior often exhibits sharp nonlinearities such as tipping points. In recent years, researchers at academic centers such as the Santa Fe Institute, as well as commercial entities such as Icosystem, have successfully studied and managed complex systems using Agent-Based Simulation (ABS), a simulation technique that captures the behavior of systems from the bottom-up. While ABS was initially studied primarily in academic settings, recently ABS has been used to solve a variety of complex business and technology problems in many industry sectors and problem areas.

ABS replicates in software the behavior of individuals, as well as their interactions with the environment and with other individuals. ABS then shows how overall system behavior emerges from these interactions, replicating complex system behaviors that cannot be captured with other analytical techniques and that are often unexpected or counter-intuitive.

Traffic is a classical example of a problem that is best captured with ABS. In particular, traffic jams are an example of an emergent behavior that seems almost paradoxical: each driver is trying to reach his or her destination, and yet traffic jams form even when there are no external factors to cause them. An ABS developed with the NetLogo softwarecan be used to show how simple driver behaviors can lead to traffic jams. In the simulation, drivers accelerate when there is nobody in front of them, and they decelerate when they approach a car ahead. Using these simple rules it is possible to replicate traffic jams that looks surprisingly like traffic jams observed under real conditions.

In the context of building energy efficiency, ABS could be used to understand how occupant behavior impacts energy consumption, by simulating occupants going about their normal daily activities and reacting to their environment, e.g., turning on lights when it gets dark (and sometimes forgetting to turn them off), opening windows or turning on air conditioning when it gets warm, using different types of appliances, and so on. More importantly, an ABS could be used to estimate the impact of various initiatives, including changes to the building itself as well as communications campaigns to encourage energy conservation. This type of quantitative approach could be beneficial in understanding how best to allocate resources to improve energy efficiency.

(This blog entry was originally posted on CityMinded.org.)

What if we took a different approach?  What if we modeled the patient’s perspective of how a person navigates the complex healthcare system in order to gain a view of their experiences and how this impacts their health outcomes?  To understand how this concept works, let’s take the simple example of traffic.  It’s very difficult to understand traffic unless you understand the behaviors of individual drivers. When you model the simple acceleration and deceleration of an individual driver, then can you start to understand some of the weird properties of traffic. For example, a traffic jam moves backward, but all the cars move forward. Applying this concept to healthcare, we can model the patient’s journey as they navigate the healthcare system seeking treatment for an ailment or choosing an insurance provider.  What emerges are insights into what tipping points arise that help or impede a patient in their interactions with the healthcare system.  Shouldn’t this be the measure of how to improve the healthcare system?

1. Wrapping a product around a pill  

For years we have heard how life science companies should focus on services around a therapeutic area and not just a treatment option.  For example, offering behavioral interventions around diet and exercise for patients with type-2 diabetes as well as a treatment option could improve patient outcomes.

2. Transparent data liquidity

Lack of data liquidity is hindering innovation among academics and life sciences.  Sorting out who owns this data and how to make it accessible will unlock its power to be used for predictive analytics.  Achieving a secure exchange of information across life science researchers and clinical care providers would increase efficiency and productivity.  For example, once this exchange of information is available in oncology, predictive analytics can be applied to help researchers gain insights from larger patient populations with similar pathologies.

3. R&D collaborations

New and innovative business models focused on IP should be created that empower anemic R&D teams.  The current investment in drug discovery, development, approval and marketing is far too large.  For example, a new model could extend collaborations from discovery through approval allowing experts who are most appropriate to a particular challenge to provide solutions.  This approach would create a virtual team where IP is shared among people focused on a specific challenge.

Could life science firms follow the trajectory of transforming themselves from product providers to service providers?  Simluations of these new commercial models using software is a low-risk way to explore the benefits of these new ideas for life science companies and for the public.

Contact us to find out more about how our predicitve analytics help power transformational ideas in healthcare.

Andy Cosgrove
Director, Healthcare Consulting
(617) 520-1032
a.cosgrove@icosystem.com

1. Model drug development collaborations that maximize IP and drug discovery

2. Simulate PRO (Patient Reported Outcomes) for care quality improvement and outcomes

3. Accelerate time to market for new therapies with strategic portfolio modeling

4. Predict market access and optimize resource allocation for new therapies

5. Predict high risk patients for ACO (accountable care organization) and hospitals

6. Leverage advanced analytics to reduce hospital readmissions

7. Simulate connected health consumer and recommend technology interventions that drive healthy behavior change

8. Simulate the financial risks and incentives of emerging reimbursement models for ACO

9. Quantify health costs & productivity of simulated workforce while recommending the most appropriate wellness intervention

Learn more about applying predictive analytics to healthcare by downloading Eric Bonabeau’s ‘A More Rational Approach to New-Product Development‘.  In this free paper, you’ll discover how Eli Lilly approached this complex problem by structuring their research to seek truth first and success second.

Nantucket Island, 2004 (Photo Courtesy of Wikipedia)

Everyone is playing (conveniently) by the rules

A Medicare rule says a state’s urban hospitals must be reimbursed for wages paid to doctors and staff at least as much as rural hospitals.  Since Massachusetts has only one rural hospital, Nantucket Cottage sets the floor for wage reimbursements across the state.  Nantucket is expensive so those wages are high, and the 49 other States in the U.S. get lower funding since the pool of money is not infinite.

Not cool and not having a positive impact on the health of others.

Doing the right thing vs. what you get measured on

The story is simple but the impact on the healthcare system is complex.  Powerful stakeholders not breaking any laws or rules and dedicated to helping people feel better are simply doing what they get measured on.  Politicians, CEO’s of large healthcare systems, doctors and nurses working at hospitals, and everyone in between are not doing the right thing because they are not good at making decisions in such a complex system.

Alone together

It is easy to point out what is wrong with the healthcare system.  So much more challenging is offering a solution.  Many individuals are offering solutions.  Many of these individuals are connected to one another and some are not.  Alone together!  Are you a disruptive innovator?  Can we improve the decisions we are making in a complex healthcare system?

Interestingly, on that same date the Freedom Tower  – also known asOne World Trade Center - surpassed the height of the Empire State Building, thus restoring the World Trade Center as the tallest building in New York. The protest passed within one block of the Freedom Tower.

The stark temporal and spatial juxtaposition of these two events made me ponder some of the paradoxical aspects of our society, and provided a sharp example of the distinction between systems that are complicatedand systems that are complex. Erecting the tallest building in New York is complicated – thousands of people cooperating to assemble millions of parts in an act of defiance to the laws of physics. However, the only kinds of spontaneous, unplanned outcomes might be the project running over schedule or over budget: the shape, height, and functionality of the building are pre-determined and executed according to a carefully orchestrated, centralized plan. If one part is defective, it can be replaced with another part and construction can proceed; if laborers fall ill or go on strike, the project may be delayed, but it will still be completed.

In contrast, a protest is largely – if not entirely – the emergent result of some simple instructions followed by individual activities, coordinated only through interactions with other individuals and with the environment. At a higher level, the general discontent that leads people to march in protest, is itself the emergent result of a complex web of events, opinions and communications, with no central director or orchestrator. And moving our viewpoint even higher, the task of “managing” an entire society, trying to keep people satisfied while maintaining a stable economy and a healthy population, is a frightfully complex and daunting task.

And even though controlling a protest may seem “simple” compared to running a country, the outcome of a protest is difficult to predict and can vary dramatically, unlike the process of constructing a building like the Freedom Tower: while the march in New York remained relatively peaceful, similar marches in other parts of the country were less orderly. In Seattle, for example, the May Day march was marred by some acts of violence and vandalism. Often times with these types of events a small triggering event can lead to a dramatic change in outcome.

One of the most elusive aspects of complex systems such as a protest, is that it is very difficult to judge what interventions might be effective in achieving desired outcomes. In New York, for instance, massive amounts of police accompanied the parade. Coupled with the use of barriers and careful control of pedestrian traffic, the result was a relatively orderly march. However, as I walked along Broadway watching the scene, I couldn’t help but feel as if the orderliness of the protest was a very delicate balancing act. I have witnessed protests where a small triggering incident suddenly turned a peaceful march into mayhem. And we have all seen pictures and videos of stampedes, where barriers can become deadly traps. Thankfully, this particular protest remained peaceful.

Follow @icopaolo on Twitter

Interested in learning more about how agent-based modeling can provide insights into new ways of doing business?  Download a free and informational e-collection from Eric Bonabeau’s “A Journey Through Complexity: Decisions”, that contains:

• “Predicting the Unpredictable”,
• “Decisions 2.0: A Framework for Harnessing Collective Intelligence”,
• “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems”.

  

To elucidate my frustration with Boston’s public transportation system, let me share an anecdote. I live in a central location of downtown Boston. Within a five-minute walk I can reach multiple T stops on all four Green Lines (B, C, D, E) and on the Orange line; there is a bus stop on a major line less than 100 yards from my front door.  From my condo, under favorable traffic conditions, I can get to Boston’s Logan airport in about eight minutes and as much as fifteen minutes on a bad day. Last night I arrived at Logan just after 8pm (a pretty busy time when many business people return from trips) and did not have my car. I decided to be good and take the T instead of a cab. At exactly 8:20 p.m. I was standing at the curbside shuttle stop for the T. It took about fifteen minutes before the shuttle arrived, then another fifteen minutes before an inbound Blue Line train arrived at the Airport station. I finally got home at 9:12. In all, it took me fifty-two minutes to travel roughly five miles! I have made the same trip on a few occasions, trying both the Blue and Silver lines, and have never made it in less than forty-five minutes.

By comparison, if you live in Washington, Chicago, London or Rome, taking public transportation to the airport can save you substantial amounts of both time and money. Not all cities have this level of connectivity, but I cannot think of any other city where the airport is so close to downtown, and so hard to reach by public transportation.

There are several factors that I believe contribute to the problems with Boston-area public transportation. First, for rail transportation (subway and commuter rail), the lines are laid out in a hub-and-spoke configuration that makes travel easy if you want to move along one of the spokes, but frustratingly difficult if you are trying to connect between any two points not along a spoke.

(click image for a larger view)

Second, I don’t believe the MBTA really understands its customers, a shortcoming that leads to a dismal experience and discourages people from using public transportation. By this, I am not referring to typical customer service complaints, such as the aging fleet with frequent breakdowns, the difficulty of getting information about a trip, or the occasional uncaring or downright rude behavior of MBTA personnel. I am referring instead to the ostensible lack of understanding of whyconsumers use public transportation, where and when they need to use it, and how they want to use it. People want to get to and from work. They want to get to places where they can conduct business or be entertained. And they want to be able to do it in a reasonable amount of time. Why is it so incredibly hard to get from North Station to South Station (the two main rail hubs in Boston)? Why is it so hard to get to the airport? Why aren’t there more preferential bus lanes to help buses avoid congestion? Why don’t buses have more regular and consistent schedules? Why is it so hard to get information about the status of individual buses and trains for adequate planning? Why does the T stop running an hour before bars and clubs close at night?

The MBTA is saddled with debt, and is understandably reluctant to make substantial infrastructure investments to change the layout of its subway and commuter rail. But there is no excuse for the customer-related problems. Raising fares and reducing service, as was recently proposed, will only make matters worse. You can’t expect people to use a service if that service does not give its customers what they want. And if you don’t understand what your customers want in the first place, you’ll never be able to improve matters.

This is a classic example of a highly complex system with millions of moving parts and rich interconnections at many levels: geographical, temporal, social and economic. As such, it is not a problem that can be solved by conducting surveys or by congregating a bunch of smart people and consultants to brainstorm ideas; and using a traditional, statistical approach to analyze data is virtually useless because it says little about how the system’s response will change if substantial changes are made. This is exactly the type of problem that can benefit from a bottom-up, customer-centric simulation that captures the rich interactions within the “public transportation ecosystem” to understand how individual behaviors lead to the observed emergent, system-level outcomes such as ridership levels, overall adoption, and usage patterns. This type of bottom-up simulation would allow the MBTA to test many different hypotheses and plans, and figure out what it’s about to get into before actually getting into it. Until the MBTA takes this type of holistic, quantitative, data-supported approach, it is unlikely that things will improve. Mr. Davey, please feel free to drop me a line – I would love to help you figure this out so that in a few years I can take the T to work as you do and never have to take a cab to the airport again.

Learn more about about how agent-based modeling can provide insights into new ways of doing business.  Download a free and informational e-collection from Eric Bonabeau’s “A Journey Through Complexity: Decisions”, that contains:

• “Predicting the Unpredictable”,
• “Decisions 2.0: A Framework for Harnessing Collective Intelligence”,
• “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems”.
  

  Image Credit: From The Open Road, Wikimedia

After a decade of fast-growing budgets, military decisions makers are now facing significant budget cuts and must allocate resources accordingly. A number of military officers and senior civilians are confronted with a down budget for the first time as decision makers.  Their mental framework for allocating resources and prioritizing needs is entirely defined by their experience of a growing budget and dramatically fewer constraints than today. As a result, the prevailing decision heuristics (the subconscious cognitive mechanisms, or “mental accounting”, by which humans make decisions) in use today are heuristics that worked in a very different environment and under very different constraints, and are unlikely to perform adequately in the new budget environment particularly during a transition period.

Not only are these heuristics not adapted to the current environment, their effects are amplified by a second set of cognitive biases usually activated in a period of rapid change. For example, a set of heuristics called availability heuristics tend to focus a decision maker’s attention to the “stuff” that is easiest to comprehend, what’s “available” at the time the decision needs to be made, the last thing he or she may have been working on, or, in other words, the “cognitively obvious”. Availability heuristics can work well when the environment is stable.  After all, if things don’t change too quickly, the focus of a decision maker’s attention today should probably be similar to yesterday’s focus of attention. But in a rapidly changing environment, things that mattered a lot yesterday may have to be relegated to the background for today’s decisions. A concrete example is the disproportionate amount of attention dedicated (and rightly so) to the force deployed and the multiple zones of US military presence, which produces, in a period of declining budget, a similar disproportionate focus on the out force when looking for cuts, while it represents only a fraction of the total budget.

The challenge of having to cut costs and continue to perform well is not unique to the military. A 2009 Booz & Co survey of executives at the largest corporations in the US shows that short-term, “available” approaches prevail (Banerjee et al., Cut costs, grow stronger, Strategy+Business, Autumn 2009). The temptation to reduce costs across the board is dominant. The figure below, which summarizes the survey, shows that it is difficult to “be strategic when the clock is ticking.”

(Click on the image for a larger view.)

A number of cognitive heuristics likely play a significant role in shaping decisions in a sharply declining budget. A non-exhaustive list would include:

Identifying the nature, scope, frequency and intensity of the budgetary decision heuristics is crucially important to the implementation of mitigating measures. Once these have been identified, cognitive aids and decision frameworks, methods and processes can be designed to correct for the systematic biases resulting from decision heuristics.  For example, default settings, red teaming and the reframing of budgetary decisions can dramatically reduce biases.

A two-stage “cognitive audit” approach would consist of:

1. Designing and running behavioral economics experiments and adaptive survey-style exercises to assess, both qualitatively and quantitatively, the heuristics at work in a variety of decisions. Metrics can be derived from quantifying the degree to which heuristics are used. Multiple experiments should be carried out over time to measure any changes.
2. Simulating the impact of the identified heuristics on budgetary decisions by building artificial agents endowed with the heuristics. The first stage identifies heuristics, while the second stage identifies heuristics with the most impact. Metrics can be derived in the simulation from measuring decision performance relative to near-optimal outcomes, which can be calculated.

Heuristics having the most impact are targets for interventions. If they can be mitigated, they constitute high return-on-investment, leverage points toward dramatically improved decisions in a sharply declining budget.

Decision-support frameworks and tools, both at the individual level and at the group level, exist to help correct the biases inherent in human decision making. The table below offers a number of examples of mitigation mechanisms for each of the biases cited earlier.

Learn more about about how agent-based modeling can provide insights into new ways of doing business.  Download a free and informational e-collection from Eric Bonabeau’s “A Journey Through Complexity: Decisions”, that contains:

• “Predicting the Unpredictable”,
• “Decisions 2.0: A Framework for Harnessing Collective Intelligence”,
• “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems”.

Predator and Reaper drone crews at the Creech Air Force Base in Nevada where a large number of drone missions are conducted, however, use removable hard drives to load map updates and transport mission videos from one computer to another, which is probably the entry vector for keylogger virus here again. But how else can you run drone missions? Want to try flying without a map? It turns out that manned aircraft resort to the same approach: pilots upload maps to their flight computers using removable drives. What’s the alternative? The cost of doing business in an environment where the most efficient practices are prohibited is unbearable, especially if the other side does not have such restrictions. Another layer of absurdity was added to the drone case when it wasrevealed by Wired again that even after the virus was discovered the Creech Air Force Base did not inform the Air Force cybersecurity unit – probably because they didn’t think it was a serious threat, or for fear of reprisal in case the infection was due to, say, military personnel playing Mafia Wars. Even though the Air Force denies this version (but many indicators suggest it is close to reality), it is again human behavior that amplified the potential security consequences of the threat. Okay, now what was the response from the DoD leadership? “Drone units at other Air Force bases worldwide have now been ordered to stop their use,” the Wired article reports.  What about the missions? How do you upload the maps? What type of tricks do you think the pilots of unmanned or manned aircraft will use to do what they need to do? They will find a way around the ban because they have to.  As a result, the fix may end up being worse than the initial problem, except now we don’t know what the counter-trick is. Great.

Along the same lines, the excellent Security Innovation Network orSINET, an organization dedicated to “advancing innovation and enabling global collaboration between the public and private sectors to defeat Cybersecurity threats”, recently announced their selection of 16 innovative security firms for 2011:

I have nothing against these firms, and I am sure that the SINET team did a great job of vetting their technological capabilities. But, just in case you haven’t noticed, they provide very little in the way of mitigating or shaping behavior.

To learn more about understanding the impact of human behavior on network defense and security, watch an example video.

Image Credit: PC Tech Notes

Global Day of Coderetreat is a world-wide event celebrating passion and software craftsmanship. Over 2,200 passionate software developers in 90 cities around the world will spend the day practicing the craft of software development using the coderetreat format.

Coderetreat is a day-long, intensive practice event, focusing on the fundamentals of software development and design. By providing developers the opportunity to take part in focused practice, away from the pressures of ‘getting things done’, the coderetreat format has proven itself to be a highly effective means of skill improvement.  Practicing the basic principles of modular and object-oriented design, developers can improve their ability to write code that minimizes the cost of change over time.

For more information, please visit:

Boston Coderetreat on December 3rd

Boston Software Craftsmanship Group

Global Day of Coderetreat

Zach Shaw is a Senior Software Developer at Icosystem Corporation.  Follow him on Twitter. Follow @zdsbs

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