Welcome to The Flux, where we hear stories from people who have asked what if questions to better understand the world and talk about how data can help tell stories that impact decisions and create an intentional impact on the future. This is your host, John Cordier, CEO at Epistemix. In a world where the flux capacitor from back to the future does not yet exist, people have to make difficult decisions without always knowing how the future will play out.

Our guests are people who've taken risks, made decisions when uncertainty was high, and who have assisted decision makers by using data and models. We hope you can turn lessons from our podcast into foresight, so you or your organization can make better decisions and create an intentional impact for others.

Hey There, welcome to the next episode of the The Flux. We're On with Jim Malone. So Jim, we're here in Santa Fe at the complex social systems conference.  Is this your first time to Santa Fe?

First time to Santa Fe and first time as part of a, uh, actually the conference as well.

What excited you about coming to this conference specifically? And, even before that, like, what made you curious about complex systems or modeling?

Yeah, I'll give you, I'll give you a little history of where I started from.

I'm an acquisition professional contracts person in the government for a very long time.

Mmhmm.

And late in my career, I'm noticing that who you contract with is not necessarily who controls the dollars; it's corporations.  And I said, I got a problem statement. So, I went to George Mason and I said, Hey, George Mason, I got a problem statement.

I have contracts, game theory, how do you contract with a factory when you have a corporation that you don't contract with,  uh, controlling the resources and they're not necessarily guaranteed or care about the output of the product. They just want backlog because that's their non security backed asset.

So as long as you hit a pretty tight margin, you get the real money for the corporation.

Right.

So, uh, George Mason economics department says, Oh, we don't do that.  Okay. Go talk to the legal guys. Cause you're talking corporations,  legal guys says, we don't do that. That's not what we do.  But right between was computational social science and they said, Hey, come on over here.

We kind of mix things. Let's try and do stuff together. 

Sure. 

And as part of that, so I decided to go for a PhD in that.  And now I'm learning more about complexity.

And that's a pretty big, you know, commitment to solving that challenge.

Exactly. It's like jumping into doing a PhD. Just jump right into it. I mean, they said basically you can tailor it. And I'm going to tailor it. I'm going to jump in and I'm going to try to tailor it. Cool. Um, and as I get more into it, complexity is really interesting. And it's exciting. And if you look at how systems work and everything, it changes your perspective. 

Right. Right. Yeah. Um, was there a story or something that got you into the study of complexity early on or, you know, something just hit you and you're like, it just made sense. 

Well, when I walked into comp science, I didn't think that it was going to dive into complexity as much. It was really the first few classes when they talk about the process and then you start to change when you read documents or you read stuff, you start to see this is really a study that allows you to do a lot more than just solving a particular direct issue problem. It lets you see all the perspectives. 

Sure. I mean, this is something that's not a linear. It's not linear thinking is going to solve the challenge of the contracting with the companies and then the share price. That there's a whole lot going into that mix. 

Correct. And there's nothing linear in anything we do.

I mean, if you look at how a car is built, it's not linear. How a car operates may be a true system connection. It's not a complex system, it's an operating system. But the engine that builds that car, the finances, the factories, all of it, it is complex because there's variables in every step. 

Right. 

And so now you start to, you look through that lens, you can't stop seeing that lens. And that's what's really interesting. Everything I look at now has some type of complexity factor to it. 

Absolutely. So, um, you know, having spent a good amount of time in government contracting, um, you know, we were talking last night about this intersection of using models to impact policy. And, you know, do you want to talk a little bit about how, you know, what would be your fantasy of how those two things come together?

Yeah, so my vision, and this is what I talk about in my paper, is that the acquisition system itself, I'm looking down is the connection of contracting officers that have their own incentives.  Congressional people that are appropriating dollars that have their own incentives.  Executives are trying to hit a product. Operators that need a deliverable.  The supply base, which have their own incentives. So if you start to model actually how the dollar flows through, if you, if you think about dollar as the metric, I'm not sure that's the metric I want, value is a very discreet, hard to define project. But if you find the one thing that you want to put as a uniform value or item to it, now you can start to build a modeling system saying, this is the value you want, these are the controls, or these are the checkpoints. Now when policy is made, it can operate like that.

Um, I actually didn't know it, but I started doing stuff like this way back in 2000 when did my master's thesis and I looked at the result of gold water nickels on the industrial base in acquisition and I actually showed in my paper how companies, corporations were buying factories at that time and at that time the companies were Lockheed Martins, the Bones, the World to build the network of census systems that the Goldwater Vicarage Act and C4I was promoting in the language. So already the industrial based network was responding faster than the government engine, such that the supply base we were buying from was already connected towards what we wanted, because they were ahead of the game.

And so if you start to see a model and you can start to see where the policy influences, how it could shape the industrial base, or how you can actually better  policy accordingly. The, the policies of firm fixed price only, or the policy of small business requirements are not effective because of the system.

Sure. 

And if you can start to see those choke points, the weakness of systems, you start getting a better acquisition, which is better value to the, to the company, to the country. 

Sure, and that that's for both the government purpose of it and the communities where those dollars are going.

Correct. Legitimate common good. It has to be met by the government. Federal responsibilities.  

Cool. So, you know, so you've been at the conference. Have there been other talks or things that you found surprising or unlocked new ideas for you? 

Yes. A lot of the, as we say, the systems dynamic which are the ABM which could be done by a better ABM.

I don't know the language. But a lot of that connected because there's a direct flow through industrial base in the dollars. It may actually be a beneficial way to look at it. I've had, I have a couple of models. It's difficult to operate in that logo. I may actually look into Mesa and try to target that.

I wrote some down notes from some of the other where they were talking how they were measuring effectiveness in the community, some things. Those may actually be a good measure of value on the back end as I build the model. So there are some very good aspects across the conference. Both in technique and in some of the subject matter.  

So, first time in New Mexico, first time in Santa Fe. What surprised you about, you know, just being here? 

I thought it was going to be hotter than it is. It's actually kind of nice in the high places. It's quite nice in here. Yeah. The community's nice. I'm very much an early person, seems like Santa Fe doesn't wake up until 7 or 8 o'clock. And when I go out there, there's nothing going on.

Mm hmm. Cool. So when you're looking at your research and how others might carry it forward. So let's say 10 years from now, people are advancing what you've done over that time frame. What do you think the big impact that this is going to have is going to be? 

And again, my goal is what is value and how to measure value in a lot of the  Complex studies, if you even look at climate change, you can start to see how the U. S. dollar or the federal dollar actually solves the problem because you're now measuring, you'd have a complex system database looking at the results out in the community.

And then you now match it up with the federal dollar and you find out whether that federal dollar influenced that change or not. Okay. And how is it spread to move and how you separate areas. We look at. The low income looking at and how are people helped, you start to see, it can target whether the engine of the federal government tied to the representative government is actually succeeding if those dollars are a value or not.

Okay. So that's what the initial punch in is that if you can categorize this database, you can now start to use it as a resource to look at federal involvement because it can connect to anything using zip code, company, product type. Like I put up that research sciences at NICS. There's a NICS for everything.

Right. National Identification Classification System for everything. So if you have a specific study you're working on that fits in one of those zones, you can now figure out what is the influence of the government dollar to achieving your project.

And it's not just something that would be a retrospective analysis.

This is something that you can do. Do on the go forward basis, which is a total game changer.

Yeah, absolutely. 

Cool and that'd be relevant for definitely government funding and policy making. But even folks that are in the private sector that are trying to do more impact investing, like trying to see we're gonna put, you know, tens, maybe hundreds of millions of dollars to work. Are we doing in the right ways? Is this gonna have our intended impact? And then it gives you a signal along the way. Oh, we're trending towards what we thought was gonna happen. Oh, no, we're kind of veering off to a different future trajectory. Um, yeah, that'd be a really powerful tool for a lot of people. 

Yeah, that's exactly right. And people are a lot better at the, uh, the data analysis than I am. Like I said, I'm an acquisition guy. I'm just playing with that stuff, trying to keep up and get this idea forward. 

So, you know, I see we're starting to work with more like the modeling community, the more heavy data science folks. 

Was there any challenges in the language that you were coming in with versus the language they were familiar with? How did you overcome, you know, coming from a less technical background to, alright, and now I'm working with this NetLogo tool, I'm going to start looking at Mesa, which is a Python agent based modeling package. 

The actual code and syntax for myself, this is where the multidisciplinary nature of CSSSA is going to help me a lot because then I can start to leverage people who can help me in problem solving areas. I had from way back when as a young kid,  when coding was starting to get good. I know the basic rules of how to code. It's a syntax. And the idea of how to look at a problem to try and put it into code. And that's where these connections in the computational social science, I think, has been most valuable to me. Because of the multidisciplinary approach. Sure. And that is very powerful. And that's what I'm doing.  I could say it was difficult to start, but because I've been able to communicate with people, I've been overcoming those barriers step by step.

Yeah. I mean, one of the people who was on the podcast earlier, uh, he mentioned 48 percent of the work is identifying this is the challenge that we're going after. 4 percent of the middle is the model, the technique, whatever it is you're using. The last 48 percent is building consensus about this is what we're going to do about it and so, you know, having that skill set where it's not just. I'm talking about this data set and, you know, this algorithm or this distribution of how we're going to, you know, do this parameter sweep. That's all, that's part of it, but then there's the, the human aspect of how this ends up getting implemented, how it can be useful, how it can help other people think. I think that, you know, people coming at complex systems or agent based modeling in particular, having multiple views, uh, coming into it, very important. 

Yeah. And that's why actually I did the visual plots in my paper. 

Sure. 

It's because I'm a very visual person. And granted, if you add the visual into the code, it slows it down incredibly.

But for those that aren't deep into it, that visual is a great way to communicate to those outside. Absolutely. It makes it accessible. Yeah. It makes, exactly. And that's why  I went to those charts, because it really put it in a frame of reference that I could just plop on anybody and they'll say, Hey, look at this. Those do look a lot alike. And they're measuring different things. I said, yes they do. So it was very well. That's my method of getting over that last 48 percent that you talked about is the visual aspect, which is going to be, I think, important with this community.  

So, uh, the podcast, it's called The Flux, named after the flux capacitor from the movie Back to the Future.

Oh, that's very cool. 

And so, one of the questions we like to ask people when they're on the podcast is a what the flux moment that, uh, you wish you could rewind the clock. It doesn't have to be in your personal life, but it could be in any historical event. If you could go back in time, do you know, a better model of how this thing might play out,  and hit go, and kind of reset. Here's this different future that we might have been able to create. What, what would you have gone back to?  What would have been that what the flux moment for you?  

Like I said, I was at the very beginning of when was being created in 2010 in terms of an actual document, how it worked.  I think a lot of the data sets were built right around the FAR and compliance based, not value based.

If we could have put some original data sets into that massive construction, there are 93 data elements in, of which only about 20, I think, or 30 are really valuable for measuring the complexity and getting value. The rest are just checking the box type things and the, and the human input into 'em actually makes it confusing data.

If we could have actually gone backwards in time and really started the date in 2010 with a better data set, you could actually have a good history through a dramatic change of  the, of the, of the federal and the, and the government dataset, actions all the way up through 2020 and covid. Did you start to build some great trends with more, more valuable data? 

But now I have to, or I, the community will have to start and try and figure out how to use those narrow data sets to link to more richer data set to get to it. Sure. And if that, if there was that one thing that I would have done is looked at FPS and G with a much more broader sense than just checking compliance.

Okay. Cool. Cool. Yeah. So, final question. We heard about your entry point into conflict systems. If there's others that are curious, so a bunch of people in the podcast might not be technical, they might have never heard of agent based modeling before or systems dynamics modeling, but they're like, you know what, I kind of think about the world a little bit differently than others. I'm not a linear thinker. I see these things and how they all interact.  What's a good first step for somebody to take in your opinion?  You jumped right into a PhD, so not everyone's going to do that. Not everyone's going to do that, right. 

To be honest, the, the, the, the, the coding tools are very accessible.

Okay. You could see, and actually a lot of them, especially in the bio, you can see things that are going on out in the real world. And you can find a NetLogo tool, the flocking birds. It's a, it's a one that everybody uses. And you can just ask the question about what you're seeing. There may be a model that's trying to pose into what's in there.

Sure. 

And so if I would do is just explore in the, the, the ABMs and ask it, ask a question and, and see if there's already one modeling it. And from there, you can just start to see and pull back the data set. You go back to the Kenneman's type one, type two thinking, everybody thinks about the first statistic that's most obvious, but once you run it through a little model, some of the other things that are less obvious start to appear.

Sure. 

And that really piques your interest about, okay. Hey, why do I always jump to the first solution? There's other solutions out there. If I just think about it a little slower and just jumping in and playing with model starts to open that up. 

Cool. Any books you might suggest for some folks? You mentioned thinking fast and slow, thinking fast and slow. 

Along those perspectives, it's a little bit of a little more is Carlo Rovelli's book is one of my favorite books in the order of time, which is based on his view of how his time actually calculated, which starts to open up about how things are complexity because time is never constant for anybody. It's not the same thing for anybody and he gets into them. There is one of his, I paraphrase it is it's perilous to avoid the perspective of the observer.

Okay.

Because if you're observing it differently than I am, that means your perspective is important  and it must be valued. Complexity starts to get into that discussion of how do I understand what they're thinking? Because their input is changing the system as well as mine, 

Right.

So there's a great book. It doesn't directly jump you into complexity, but it starts to get into those fringes of what's important about trying to understand complexity,  which is cool. 

Well, Jim, this is a great, great show. Thanks for being on.

My pleasure. It was a good time.  Thank you.