Failure is trending right now. In the startup world, failure is a war story to be distilled and shared. In international development, it’s a traditionally taboo topic that is finally breaking through decades-long denial. A Google Trends search of “fail fast” shows steady growth accelerating to a peak in 2014. As a global society, it seems that we are shifting our mindset to embrace our failures so that we can learn from them and grow.
There’s a narrative arc to our mass acceptance of failure that seems to mirror our thinking process at the individual level. These days, understanding the social dynamics underlying our decision-making processes is as important as the decisions themselves. If we want to create social impact, we have to measure social dynamics in complex systems. And a new generation of measurement tools and methods pioneered in fields like decision science and cognitive computing is cracking the black box of human reasoning wide open.
As we’ve elevated the importance of measuring human factors at scale, researchers are moving beyond traditional social science tools to gather human knowledge. Qualitative research tools like field observation, interviews, and focus groups are deeply insightful, but inefficient for analysis and comparison at scale. Quantitative tools are more efficient but more limiting because of their rigid format. Surveys with multiple-choice answers, for example, can’t represent the nuance of human logic with the richness of a qualitative tool like cognitive mapping, in which a person creates a visual representation of how they mentally understand their environment. We can’t boil down human knowledge into a set of polling questions, and our assessments of large-scale social dynamics have suffered for it.
According to IBM, we’ve gathered at least 90 percent of the data available today in the last four years alone. This has led to a new generation of big data research tools that can glean understanding from massive, sprawling datasets. Hybrid fields like behavioral econometrics combine big data tools, behavioral psychology theory, and socio-economic data to try to understand and predict human factors like cognition and behavior. The quantified self-movement—using tools such as Affectiva’s emotional quantification engine, based on facial recognition, and Fitbit’s biometric monitoring approach to personal health—uses big data analysis to explore human behavioral data. These big data approaches use visual network analysis and predictive models to understand, manage, and forecast social dynamics.
At our design lab and consultancy Thicket, we use the big data version of cognitive mapping called “fuzzy cognitive mapping,” which quantifies and aggregates human insights about a complex topic or system. We’re applying this mapping to gather human knowledge at scale and turn it into visual analyses, decision scenarios, and predictive models for situation and policy planning, as well as for performance management and evaluation. So far, we’ve used fuzzy cognitive mapping to test business models, evaluate social impact, and monitor the performance of a healthcare incubator’s portfolio.
But big data tends to excite and terrify in equal doses, because it is reshaping our public and personal lives in ways we don’t yet understand. Because of its rapid emergence in the commercial sphere and still limited understanding in the public sphere, organizations and institutions are using big data to amass and apply vast swathes of information about their customers and members with minimal regulation. Individuals are signing away permissions to their personal data with a click of their mouse and quickly losing their grasp on personal privacy, security, and autonomy without even knowing it—see Facebook’s mood manipulation experiment for some of the complex ethical implications.
So if emotional exchange is the currency of social impact, we must also address the distribution of decision-making power. Creating impact in complex systems requires that we use not only new measurement models, but also inclusive design and management models that engage all stakeholders within a system. A multi-disciplinary lineage of thinkers—including Paul Farmer (Pathologies of Power), George Lakoff (Moral Politics), and Nicholas Christakis and James Fowler (Connected)—is helping unravel the invisible power dynamics at play in social systems. As a result, the organizing principles underpinning many of our social problems are coming into focus. For example:
- We’ve discovered that it is not easy to overcome the consequences of destabilizing and demoralizing cultures. Development programs in which post-imperialist countries return a portion of plundered resources to post-colonial countries in the guise of international aid do little to restore economies.
- Uncoupling the concept of addiction from the war on drugs has foregrounded the role of loneliness and social isolation in creating dependency.
- The limits of conventional health care and the resulting skyrocketing costs are changing our approach—from treating disease to creating health—and broadening the focus of healthcare programs to include not just a person’s biology, but also social determinants of health.
In piloting new systems change tools and methods over the last year, our team has found that both the tools themselves and how we deploy them matters. We’ve found inspiration in formats like reflection circles, which can help individuals overcome the trauma of gender-based violence, and mad maps, which structure one-on-one dialogue for decision-making and address the power imbalance that people labeled as mentally ill face.
Last year, we worked with the nonprofit Chinatown Youth Initiatives (CYI) to create a participatory impact evaluation that would serve both the organization and its members. We created a cognitive mapping workshop and evaluation process that measured the impact of CYI on its members. The maps showed members how their social environments affect their emotional health. Then, in aggregate, we compared the impact of CYI to other social environments. By simultaneously delivering insights to both the members using it and the nonprofit serving them, we aimed to create a transparent evaluation process that would lead to stronger alignment between the two stakeholder groups. Based on participant feedback, the process allowed CYI members to better understand the organization’s role in their lives and clarify their motivation for being involved in the organization.
Participatory evaluation is one way to rectify massive power imbalances and provide a structured space for individuals to engage with institutions on a level playing field. It is part of a larger set of inclusive design strategies that, when coupled with human factor measurement tools, present an enormously scalable approach to creating social systems impact—and we need scale when more than seven in 10 people around the world live on less than $10 a day.
Today, more than 70 percent of us have minimal access to the building blocks of human development: resources, training, and decision-making power over our lives. And yet, human networks continually create opportunity. We have all the tools we need to shift our systems and structures to a more equitable balance and quickly. Now we just need to do it.