The third GSBE Marketing-Finance Symposium, hosted by the School of Business and Economics (SBE) and the Graduate School of Business and Economics (GSBE) at Maastricht University, took place 8 October.
Paras Anand, Head of pan-European Equities at Fidelity Worldwide Investment—the main sponsor of the event—delivered a presentation on decision making within complex systems, and spoke with us about the importance of sharing knowledge across disciplines.
Paras Anand is a firm believer in life-long learning and bridging disciplines, which he says is what attracted him to speak at the recent GSBE Marketing-Finance Symposium. “When I went to university, there was this kind of almost snobbery about marketing in the context of academics,” he says. “The economists thought they were the serious social scientists and that marketing was much more of a vocational pursuit.”
“But actually,” he continues, “what time has proven is that people who do marketing actually understand consumer behaviour, and this is actually very informative to people who are trying to think about and do economics seriously. So any course or approach that tries to synthesize different disciplines and looks for links is a fantastic thing.”
Anand says that today especially it is important to dedicate yourself to reading and staying informed widely outside of your field. “It’s always been relevant,” he says, “but I think it’s even more so now, when the points at which one profession ends and another starts are blurring.”
What academics and practitioners can learn from each other
The same is true for the line between practitioners and academics—areas Anand believes can both gain from sharing knowledge. “We as practitioners look to research in order to find models and approaches that we build upon a little bit ourselves and that help us to become better investors,” he says.
“Academics are searching for the application of the theory. And the practitioners are looking to the theory to help them with the application. So what you hope is that academics also can learn about which parts of their research are going to actually end up having impact. Because that’s where the value of research is, when it actually has a societal impact, not where it kind of ends up becoming somewhat circular.”
What ants can teach us about complexity
The notion of learning from other disciplines and applying knowledge from various areas was well represented in his symposium talk on decision making in the context of complex adaptive systems. To start, when discussing complex systems, Anand asked the symposium attendees to consider ant colonies.
“In an ant colony, the ant can effectively choose one of three jobs on offer,” Anand says. “They can forage, they can patrol, or they can do midden work. And they basically choose for themselves which of these jobs they want to do and, importantly, they kind of switch those jobs over time. There’s no strategy, no overarching plan, and even people who study ant colonies have a hard time explaining why individual ants behave the way they do.”
But when observed at the overall level, Anand says, the ant colonies are highly intelligent, highly sophisticated organisations. “They can solve problems, they build nests, they protect their young, they do all sorts of amazing things at the overall ant-colony level.”
The example of the ant colony illustrates how much complex systems are a part of our everyday lives. “Your immune system is a complex system. The Japanese stock market is a complex system. As is the city of Frankfurt,” Anand said. “Complex systems characterize our lives, our societies, but we are poor at recognizing and dealing with complexity.”
Making decisions in the context of complex systems
Complexity situations arise, Anand explains, through a series of factors. “First, you have a bunch of individual agents in a system, and they make independent decisions about how to behave. And they switch these behaviours over time,” he says. “The second factor is the individual agents in a system interact with each other. The third is what scientists call emergence. That means that out of these multiple interactions there arises a single, observable entity that behaves in a way that it can almost be independently monitored.”
The challenge is detecting cause-and-effect relationships within these system, Anand explains. He illustrates this with a story about Yellowstone Park where, in the late 19th Century, the US cavalry was brought in to run the park and thwart poachers who were killing off the park’s game. The elk population grew massively as a result, and began overfeeding, stripping back the flora and fauna and undermining the aspen trees. The elk subsequently devoured the aspen trees, and without aspen trees beavers could not build dams, which then imperiled their growth. The dams were important in keeping the waterways clear and without this, the trout and other river life began to perish.
“What you can see from this example,” Anand says, “is that when you have incorrectly identified cause-and-effect relationships, individual decisions end up having massively painful consequences, so the stakes are incredibly high in terms of making decisions in the context of complex systems.”
Deepening our understanding of complex adaptive systems
Why is understanding decision making within complex systems so important? “We deal with complex systems every day, and the world is only getting more interconnected. Although, thanks in part to the work of academics, we are getting better at thinking about and understanding complexity, in general people still have shown they are very poor at their ability to be able to calibrate and deal with complexity.”
In the practical world and academic world, Anand says, this is problematic. “Classical economics, classical economic theory, tries to create a framework by making the assumption that we make these rational decisions between things like leisure and work, equity markets and consumption and savings, and luxury good and value and utility,” he says. “But of course if this were true, nobody would buy luxury goods or become a workaholic, and nobody would invest in anything as unpredictable as the stock market.”
A framework for dealing with complexity in investment decisions
So how can one make investment decision in the context of complex adaptive systems? Anand has develop a four-part framework, which he says is based on “ideas borrowed from people I admire, all ideas that I put to my team when advising them on making decisions on behalf of clients or assessing investment decisions.”
- Learn from calculus (as well as statistics): “Be respectful to the principle of calculus, that you take a series or an equation and you rearrange the proponents in order to find a certain configuration of the equation that yields a certain amount of insight that allows you to define what the next steps are. Statistics is often a very linear process. The great thing about solving a problem in calculus is that you don’t know what the right way to solve it is until you go through the process of rearranging.”
- Checklists: “Checklists are very, very helpful for people who are doing tasks in complex systems where there’s significant downside risk in the outcome. It’s a very simple way to eliminate small mistakes or easy mistakes by ensuring that all bases that you would expect to be present are covered. So we use a series of checklists when trying to see what the indicators are that things are going wrong.”
- Use the outside view: “The outside view runs in contrast to our normal problem-solving methodology, which is what we call the inside view, in which you say, I have a problem, I collected all the related facts, I analyzed those facts, and have established a way to move forward. Instead, when you have a problem, do not look at it in the context of that problem, look at it as an example of a wider reference class. Think: when we’ve been in this kind of situation before, what kinds of things have happened?”
- Accept multiple outcomes: “When people make decisions, they really fixate on one potential outcome, and they don’t truly believe or invest in the idea that there might be multiple outcomes, hence they don’t really ascertain risk/reward properly. Thinking about the world panning out in different ways, and actually going down the paths of the different scenarios, can be very helpful in actually understanding what the downside or upside risk is of any investment decision.”