When I was at Rocketdyne, my last job was to research, test, and (if warranted and reasonable) deploy social media and collaboration technologies. Part of the reason I took the early severance package they offered back in 2010 was because I didn’t believe the company was really commmitted to supporting what I was doing.
Now it looks like I’m going to have to resurrect my knowledge of those tools and platforms just so I can interact with my friends and family. For instance, anyone who sees a lot of my posts on Facebook knows I usually go to the gym on Fridays, then out to dinner and for a couple of craft beers with two of my former colleagues from Rocketdyne.
We can no longer do that for the next month or so, and we’ve already talked (texted) about how to get together virtually. Not sure how, but there are lots of options. I’ve been using Slack with Quantellia, but I’m really interested in something free. I’ve used Google Hangouts before and I’ve been reading some good reviews from Zoom users. I don’t think Zoom existed back then, but I’m going to find out about it.
The saying is “necessity is the mother of invention,” and I have no doubt the next few months are going to drive our innovative capabilities and our need to collaborate and work together. While I’m not looking forward to being essentially cooped up in my house with my wife and two teenagers (plus a dog and two cats) I am a little excited about discovering the positive things we can extract from the disruption. I expect there will be far more than most of us can contemplate. Hang in there everyone. Let’s expand that silver lining.
I have no doubt I am a very lucky person. Although I do not have an education in any science, I was able to spend approximately two decades working on the Space Shuttle Main Engine (SSME) program at Rocketdyne (through four major aerospace corporations). I spent a lot of time working with some of the brightest rocket scientists (for realz) as well as world-class engineers and scientists in literally dozens of disciplines.
Since my retirement from (what was then) Pratt & Whitney Rocketdyne, I have worked intermittently with Quantellia, LLC, an artificial intelligence / machine learning software development firm. Needles to say, I have no formal education in any computer field, with the exception of two Visual Basic classes I took at a nearby Junior College. I was introduced to one of the co-founders of Quantellia shortly after my retirement. She showed me a tool they had been developing called “World Modeler”. It was the most exciting thing I’d seen in a long time, and I was especially impressed with how it brought a highly systemic approach to modeling and forecasting in complex situations. I ended up writing several papers and a bunch of case studies for them.
In 2015 I returned to work at what was then Aerojet Rocketdyne (still is, for now) where I worked on a small rocket engine program for a little over two years. After leaving, I started doing some selling for Quantellia and, beginning in March of 2018, I became the company’s Business Manager, a position I’m still working at.
Last year we held a summit, in conjunction with SAP Global Services, at their Labs in Palo Alto. It was called the “Responsible AI/DI Summit.” In this context AI stands for “Artificial Intelligence” and DI stands for “Decision Intelligence.” One of the main purposes of the summit was to discuss how we can develop artificial and decision intelligence such that we concentrate on using them to solve humanity’s most “wicked” problems, rather than merely work at developing apps, the main purpose of which is to make money for the developers, investors, and entrepreneurs involved in the business.
Below are some of the folks who worked on the Summit, including me (the long-haired guy in the middle of the back row). Also, here’s a link to this year’s second Summit – Responsible AI/DI Summit 2019, as well as a link to the RAIDI Blog.
Quantellia and SAP folks who worked on putting it all together
As I learn more about machine learning, artificial intelligence, and decision intelligence, I will work at sharing my knowledge and understanding of these tools, and the issues they raise. I know the people I’m working with are dedicated to serving humanity, not merely milking it for profit. That pleases me and I hope we’ll be able to prove we’re doing the right things to ensure such service continues to exist and grow.
I know it’s been quite a while since last I posted here. I’ve been continuously active on Facebook and have begun tweeting quite a bit as well, but that’s not why I haven’t posted to this blog in the past nearly three months. As of March 1 I began a new career, probably not the kind of thing you hear about 70-year-olds doing all that often. Since then I have been working as the Business Manager for Quantellia, LLC. You may recall I’ve done work for and with Quantellia on and off for the past six years.
Quantellia is a small AI/ML software development house and, until now, one of the co-founders has been running the business. Inasmuch as she is also the organization’s Chief Scientist, and a well-known pioneer in Machine Learning, this was not exactly the optimal thing for her to be doing. I had been touching on the subject and, since she was having such a hard time getting someone competent to run the business, I pressed my offer to do so. She finally relented and things have been going swimmingly, although there have been times I was swimming against the current. I’m definitely climbing a steep learning curve, which sometimes has me questioning if I’m losing my edge.
Actually, at times I can’t quite tell if my intellect is slipping a little bit, or if I just don’t care as much as I used to and I’m not quite as arrogantly sure of myself. My memory seems to be intact, along with my ability to learn and adapt. I’m going to go with the “I just don’t care as much about things as I used to; I’m more sanguine about life, work, and the need to control everything.
At any rate, I’m having a lot of fun. I was once partnered with two CPAs, doing royalty accounting for some big acts: Jackson Browne, Joni Mitchell, The Cars, Dollie Parton, Ronnie Milsap, The Commodores, even Jimi Hendrix’s estate. I learned a fair amount about accounting back then, and now I’m getting the opportunity to revisit what I learned, applying it in different circumstances. I’m also learning about artificial intelligence and machine learning, and hope to convey some of what’s going on in these fields. Although not a data scientist, I am quite capable of seeing where AI can be applied in business to assist with all kinds of issues. I’m sure you can as well.
Having worked with Dr. Pratt and her company, Quantellia, I have long been convinced their approach to decision making is one of, if not THE, best methodologies I’ve encountered. After what I consider to be one of the most disastrous general elections in my lifetime, it would seem we need help in navigating the complexities of the world and our place in it. Lorien’s work can, I believe, help us understand the consequences of our decisions, before we make them. I urge you to watch this video and become more conversant in the issues Dr. Pratt raises. What follows below the video are some of the “liner notes” that go with her TEDxLivermore talk.
Making decisions based on invisible inputs is like building a skyscraper without a blueprint. Yet that is the norm, even for very complex problems. Contrary to how most of us think about making a decision as being the act of choosing, a decision is the last piece of a long, almost completely invisible, process. The good news: it is possible to make the invisible part of decisions visible.
In working with the Community Justice Advisor Program in Liberia, Africa, Lorien and colleagues helped The Carter Center (founded by Jimmy and Rosalynn Carter) use decision models to increase positive outcomes in the domain of civil justice, by identifying the most effective levers for change.
Using deep learning artificial intelligence, the interconnections between inputs become visible, and unintended consequences can be identified before implementation. Vicious cycles can be reversed, and virtuous cycles of improvement can be built in place and nurtured through intelligent decision metrics.
As co-founder of Quantellia, Dr. Lorien Pratt co-created the decision intelligence methodology and the company’s award-winning World Modeler™ software. She consults and speaks worldwide, and is known for her neural network research and the book Learning to Learn. A former college professor, Pratt is widely known as the former global director of telecommunications research for Stratecast, a division of Frost & Sullivan. A graduate of Dartmouth College and Rutgers University, Pratt holds three degrees in computer science. She received the CAREER award from the National Science Foundation, an innovation award from Microsoft, and is author of dozens of technical papers and articles.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
In an effort to improve my “working out loud” chops, I’m learning from a friend who has begun sharing the text of (not links to) his blog posts on Facebook and LinkedIn, as well as on the blog he’s had for a very long time. <Light Bulb!> This one’s a kind of reverse emulation, as this is something I shared on Facebook first.
I have found an interesting difference of opinion on the subject of simplicity versus complexity, but it seems to hang on what dimension of endeavor we’re looking from. From an engineering design perspective – especially wrt products for the consumer market – there’s evidence complexity (think shiny objects) is actually a better seller than simplicity.
It seems to me, however, that da Vinci was looking a little deeper than marketing prospects and was more interested in the aesthetics of design . . . all kinds of design.
So . . . I’m thinking of it in terms of this software tool I am now representing, called World Modeler, which is used to model the elements required to make important and quite likely expensive organizational decisions to better . What we (Quantellia, LLC and I) can do is transform highly complex decision models (involving numerous decision levers, external factors, intermediate effects, interconnections, and even qualitative assumptions) to graphically (and quite simply) show how they will play out over time given certain values. The goal is to render the complex simple, not to simplify that which is complex.
World Modeler adds a Systems approach to Project Management
In my last post I took a stab at defining, and explaining, the concept of Decision Intelligence. I’m willing to bet you’re going to be hearing a lot about it in the not-too-distant future. So you don’t have to click back and forth, I’ll copy over what I wrote about it in that post:
This is the term Quantellia now uses to describe what it is we do. NB – The term is not “Decision Analytics”; there’s a reason for this. Perhaps it is best understood when one looks at a part of how decision modeling is accomplished. Part of the raw material available today for making decisions is what we call “big data”. There’s an awful lot of attention being paid to the field of predictive analytics, which uses big data as its raw material. We at Quantellia prefer the term predictive intelligence. This is because predictive analytics uses past performance (data) to project trends into the future. We like to think we take the concept a bit further.
While we believe analytics are useful and important, they lack the dimensions of human knowledge and understanding that can more completely predict how the past will play out in the future. A subtle distinction? Perhaps, but I find it a valuable one. Unless we’re talking about the future activity of a machine designed to perform a very limited set of instructions or actions, our activities involve human understanding, emotion, and interpretation. There are times when these attributes can dramatically change the course of an organizational effort, rendering previous decisions moot or, at best, only partially useful or correct.
By providing a method whereby human understanding, intuition, and wisdom can be incorporated into the decision model itself, we believe we can more intelligently predict the future. We are well aware there is no such thing as infallibility. However, we also know the more useful and actionable information and knowledge we have available to understand what has happened − and is likely to happen − the better our decisions will be.
Now, having had some time to think about it – it’s been over a month since that post -and having discussed it a bit with Quentellia’s Chief Scientist, Dr. Lorien Pratt (@LorienPratt), I’d like to add a little something to both the definition and the description of what World Modeler has to offer. Keep in mind, as with many things, perhaps even more so with something truly emergent and reasonably new to my experience, both my understanding and my ability to explain are evolving; developing structure and nuance as I learn more theory and encounter more examples of real-world situations.
I consider systems thinking, or the ability to see systems — and systems of systems — as the most effective way to understand what is happening within any one or more of those systems, as well as have a chance at affecting the outcomes of the ones designed to produce value and realize valuable results or consequences of their workings. The more elements of a system that can be modeled, the more likely you will be able to understand downstream effects of your decisions, and the more likely you are to see the unintended consequences of actions before you take them.
Here’s where Quantellia’s World Modeler™ excels as a decision modeling — and making — tool and enabler. Consider Predictive Analytics, the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. PA usually returns fairly simple, pairwise relationships, e.g. these customers in this demographic, with this amount of revenue, etc. are likely/not likely to churn or devoting a certain amount of energy to customer retention is likely to affect/not affect customer churn.
World Modeler, on the other hand, allows you to create a highly complex systems model. This means you can look at numerous elements and their interrelationships to see how they work together, e.g. customer characteristics, customer retention efforts, likelihood to churn, total customers, revenues, and even business rules that might have to be taken into consideration if certain levels of activity are reached. Furthermore, when you don’t have data for one or more of these elements, you can use human expertise, the tacit knowledge of your employees or the group to fill in the gaps. When you have real data, if you later are able to gather it, you can then plug it into the model and continue going.
One more thing. World Model is a highly flexible, iterative navigation mechanism. It allows you to predict without complete or perfect knowledge, then pivot and change the model as new and/or different knowledge, information, and data are gathered or encountered. You can do this repeatedly over the course of months or years, whatever’s necessary to help you make the best decisions for achieving your desired outcomes. So success doesn’t depend on long-term predictions. Rather, it depends on navigation and alignment between the organizations systems, processes, and the humans that employ them.
Now . . . having learned all that, aren’t you interested in seeing how this tool works? You can get a free evaluation copy and all you’re giving up is a little contact information. There’s no obligation. Click on this the link to download a fully-functional two-week evaluation copy of World Modeler. Give her a Whirl(d)!
While I have written a little bit about one of the new endeavors I have set out to pursue (here and here), I haven’t really done much to explain what it is I’m doing with decision modeling and my work with Quantellia LLC. I am in the process of writing a post about some of the concepts I’ve been looking into and learning about, but it won’t be ready for a while, as I have more studying and research to do.
I do, however, have the ability to share some of the material I’m learning from, as Quantellia has produced a significant number of videos and recorded webinars. This one is the one I usually send to prospects. While it is the oldest, it’s also one of the shortest and still conveys the essence of what Quantellia, and it’s product World Modeler, can do for a business or organization facing complex decision-making.
So . . . I’m not sure if I actually announced it here on my blog, but as of the beginning of this year I have become a referral partner for Quantellia. In my opinion they have not only a superior product, but a superior mindset regarding how decisions are made. As a systems thinker I am keenly aware of the value in a long-range, strategic, informed approach to deciding how to proceed and to keeping track of what’s happening, always being prepared to take a different path if circumstances warrant it. I believe the people of Quantellia do exactly that and that World Modeler is a tool that makes it much easier to accomplish.
If you have an important, complex decision to make you need to understand how decision modeling works. As Dr. Pratt says on the video, you can model many decisions using paper and pencil, but you can’t do a good job of it without understanding how to “engineer” the decision using more than just analytics and predictions based on them. You need to use “Decision Intelligence”. Quantellia can help, which means so can I. Please let me know if you’re interested in discussing your specific needs. I’d be happy to set up a teleconference to see if we can help. Thanks.
PS – I’m going to share more of these videos here, but you can see them all for yourself at Quantellia’s YouTube channel, located here.
In a previous post I mentioned some work I had done for Quantellia involving the Carter Center’s efforts in Liberia to strengthen the country’s legal system. I have not been at liberty to discuss the effort until a couple of weeks ago, when Quantellia announced the work and their findings. Of their work, the Carter Center says:
Since 2006, building on its long history of engagement in Liberia, The Carter Center has been implementing an access to justice project in Liberia in response to these critical needs and invitations by the government. Governed by a Memorandum of Understanding with the Ministry of Justice, and in partnership with the Ministry of Internal Affairs, the National Traditional Council, and other stakeholders, the Center works in four areas with the aim of helping to create a working and responsive justice system consistent with local needs and human rights, paying special attention to rural areas and the needs of marginalized populations. ¹
One of those four areas mentioned is “Improving Access to Justice”, and Quantellia was tasked with building a decision model showing the efficacy of sending Community Legal Advisers (CLA), now called Community Justice Advisers (CJA) out into remote, underserved communities by providing them training, support, and motorscooters. These CJA are paralegals and they are tasked with helping both plaintiffs and defendants gain access to the formal justice system which, in many locales, lags behind the people’s use of customary justice.
I want to share the results of that work here. I’m very pleased and proud with the role I was able to play in the final document. The agreement was that I would do research and write a first and second draft, at the least. Also part of the agreement was that I would not receive credit, which I was quite happy to accept. I am, therefore, grateful the authors saw fit to acknowledge my efforts in a footnote. It’s far more than I expected; a lagniappe.
Here’s a link to the World Modeler Blog, where you can read Quantellia’s announcement regarding the project. Although both the paper and the video are available there, I’m also including a link directly to the paper (here) and embedding the video below.
I have often said I thought I would find it hard to find something to do that would be as exciting and fulfilling as working on the manned spaceflight program — specifically the Space Shuttle Main Engine — which I did for over two decades before my (somewhat early) retirement. After all, working with many of the world’s best rocket scientists does have its perks (or perqs), especially intellectually, and being a part of humanity’s effort to venture out into space is something I feel borders on the sacred. Working on this project provided me with those feelings as well and was both challenging and fulfilling. The video and the paper are, in my opinion, very well done and beautifully presented. I am proud to have been a small part of it.
I am, of course, very supportive of Quantellia’s vision and the products and services they have to offer. In fact, in case I haven’t mentioned it elsewhere, I began an association with them as a referral partner at the beginning of this year. If you’re dealing with complexity and would like to hear how we can help you realize your goals more effectively, drop me a line. I’m easy to find.
Although I am not trained as a scientist or an engineer, I did spend over 20 years working with engineers and (yes, Virginia) rocket scientists. I also may not be a professional scientist, but I’m a pretty good amateur one, and I like to think that being around all that knowledge and brain power bestowed on me at least a patina of engineer/scientist. I do know I am loathe to make decisions without a great deal of information and as much knowledge as I can locate about the consequences of my decisions.
One of the methods we used to make engineering design decisions is called a trade study, which is short for trade-off study. It’s essentially a very simple concept, whereby you develop the desired outcomes, e.g. features and capabilities of a system, schedules and cost structures, break them down into measurable parts, and compare various ways to achieve those outcomes. The process itself is reasonably simple, but the details can become staggeringly complex and frequently overwhelming.
Expertise System Trade Study Selection Criteria
As I learn more and more about Decision Science and its derivatives, I look for examples and analogies from previous experience that I can revisit with newfound knowledge and capability and, perhaps, understand a little more clearly or completely than I did back then. I am convinced Decision Science, which embraces the concepts of Decision Engineering and Decision Intelligence, as well as the use of Decision Modeling to pull it all together, is a powerful tool that too few people know about.
One of the things that occurs to me, and I want to throw this out for your consideration, has to do with the remarkable software tool Quantellia has created, called World Modeler. It seems to me it is to trade studies what AutoCAD is to hand-drawn blueprints. It is so rich in modeling functionality, it just makes every other tool I’ve worked with seem flat, unimaginative, and terribly cumbersome in terms of what we know we’re facing and how we see problems in today’s environment.
Just after the turn of the century (that would be 2001) I conducted a trade study for a software tool, some of the requirements of which were that it would run on our intranet and was designed to both locate expertise and facilitate the exchange, use, and capture of knowledge in a form that would remain useable for some time to come. In short, one kind of knowledge management system that was being sought after back then.
The trade study I conducted looked at three products and rated them over about a dozen criteria we were interested in. There was no formal weighting and it was a fairly simple, straight-forward comparison of capabilities. A simple effort based on what we knew at the time and the very palty list of vendors who were providing the kind of service we wanted. In fact, as you can see in the accompanying graphic, there were only three . . . and one of them (Primus) was actually — if memory serves — an internal service of Boeing’s developed specifically for airplane mechanics to share information on their service and repair activities worldwide.
With World Modeler, we would have been able to model so much more than just these paltry selection criteria. We could have included in our decision the aggregate likely impact of things we assumed would happen, thereby surfacing possible misconceptions that some had. We could have included connections to IT, HR, and Communications, thereby giving us a clearer picture of the likely impact on the enterprise of implementing the system.
Frankly, in retrospect — and based on the fact that its use steadily declined after my retirement — I’m still not certain it was a good decision, though I had lots of reasons to believe so back then. So, getting back to my point, I think World Modeler is an incredibly powerful tool for an organization to measure the value of many of its decisions before making them. It’s also useful, in my opinion, in a wide array of situations and scenarios. I intend on addressing many of them as time goes by and I become more versed in its capabilities and more comfortable with my understanding of its value to various organizations and situations. You should check it out.
Funny how some things seem — given enough time — to come full circle. Although I have always seen patterns and complexity, as well as the intricacies of their interplay, I wasn’t introduced to the concept of Systems Thinking until I worked on the Space Shuttle Main Engine program at (what was then) Rockwell International’sRocketdyne division. That introduction included being exposed to the thinking of Dr. Russell Ackoff, a recognized authority in the field. I was fortunate enough to spend some time with him, twice in Philadelphia, prior to his death in October of 2009.
Shortly after retiring from Rocketdyne in 2010, I was introduced to Dr. Lorien Pratt of Quantellia, LLC, who showed me a tool her organization had developed called World Modeler. I was excited at what I saw and hopeful I could somehow become involved with Dr. Pratt and her team. However, that was not to be at the time. I was provided the opportunity to more thoroughly investigate the tool, but I had made a conscious choice to refamiliarize myself with Apple products (after over two decades of living in the PC, DOS, and Windows environment) and World Modeler was not written to be run on a Mac. Furthermore, the PC laptop I had wasn’t powerful enough to do the math and drive the graphics required for running models in real time. I was hosed.
Decision Intelligence Technologies
Finally, about six months ago I was contacted by Dr. Pratt, who asked me if I wanted to assist in writing a paper that described an effort in which they were involved with the Carter Center. I enthusiastically said “Yes!” I’ve done a couple of other things with Quantellia since then but, beginning a few weeks ago, I took on an entirely new and (for me) exciting role as a referral partner.
Right now I’m spending a fair amount of time learning Decision Science in general, and the process and tools Quantellia uses to help organizations understand complex interrelationships and make better decisions based on that understanding. As I’m doing this I watch videos, read blogs and articles, look for original research, and work on presentations that will help me educate others in this important approach to business and organizational operations.
So . . . here’s the full circle part. As I’m looking for definitions, or explanations, of Decision Science and its origins, I Google the term. The first two hits I get are to The Decision Sciences Institute and to Carnegie Mellon University’s Department of Social and Decision Sciences. The third link is to a Wikipedia article on that same Department. In that article, there’s a link to Decision Science, specifically. However, it redirects to an article on Operations Research, which is where Systems Thinking originated. At the bottom of the page is a list of researchers under the heading “See Also”. One of the researchers, unsurprisingly, is Russell L. Ackoff. To me, that’s a combination of serendipity and years of working on better understanding how an understanding of systems can work to the benefit of any organization; actually, anyone.
I’ll be writing a lot more about Decision Science, including my understanding of some of its constituent parts, Decision Intelligence, Decision Engineering, Decision Modeling, and the power and value of our tools, World Modeler and DEEPM (Decision Engineering for Enterprise Project Management). I hope I will be able to clearly explain what it is we have to offer and, more importantly, what everyone has to gain by understanding it. The value exists independently of me or even Quantellia. We’ve just been at it for a while and can apply and employ the discipline both efficiently and effectively. Stay tuned.
I know it’s been quite a while since last I posted here. I’ve been continuously active on Facebook and have begun tweeting quite a bit as well, but that’s not why I haven’t posted to this blog in the past nearly three months. As of March 1 I began a new career, probably not the kind of thing you hear about 70-year-olds doing all that often. Since then I have been working as the Business Manager for Quantellia, LLC. You may recall I’ve done work for and with Quantellia on and off for the past six years.
Systems Savvy 
