From Communities to Topologies, Forecasting is a Social System

Abstract

Making a forecast is a social act. Forecasting papers in the literature tend to focus on the mathematical or computational aspects, but they often provide a diagram of the forecasting system that includes other social components. These diagrams codify implicit social systems, or at least parts of them, with some indication of the flow of information through the network. A review of these networks shows a very wide range of topologies, from near-well-connected networks to linear or cyclical networks. Diameter increased with network size, implying that more expansive systems could have less efficient flows of information. The exercise of diagramming the social system that forms a forecast can help guide forecasting programs and improve efficiency, accessibility, and equitability.

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From Communities to Topologies, Forecasting is a Social System
Nicholas R. Record1
Abstract
Making a forecast is a social act. Forecasting papers in the literature tend to focus on the mathematical or computational
aspects, but they often provide a diagram of the forecasting system that includes other social components. These
diagrams codify implicit social systems, or at least parts of them, with some indication of the flow of information through
the network. A review of these networks shows a very wide range of topologies, from near-well-connected networks to
linear or cyclical networks. Diameter increased with network size, implying that more expansive systems could have less
efficient flows of information. The exercise of diagramming the social system that forms a forecast can help guide
forecasting programs and improve efficiency, accessibility, and equitability.
Keywords: ecological forecasting, topology, forecasting cycle, social system
1Bigelow Laboratory for Ocean Sciences, Tandy Center for Ocean Forecasting, East Boothbay ME, USA
nrecord@bigelow.org
Introduction
“Does the removal of urchins and perrywinkles lead to brown water?”
This question is one of hundreds of hypotheses I have heard over the years working with community groups on forecasting
projects. It reflects one of the reasons that community-centered science is so engaging. The ideas I encounter in
communities are far more wide ranging than what’s found among scientists—who are also interesting, but are often much
more in lock-step with each other (McClenachan et al. 2022). Questions and ideas that come from communities can broaden
the scientific perspective, but they can also give an indication of what’s important to that community. This resonates at a
time when we know that science needs to be more equitable and inclusive. It is especially pertinent to environmental
forecasting, which can have direct impacts on communities.
So how can a community’s knowledge shape a forecasting system?
Thinking about this question took me down a strange rabbit hole recently. Or maybe it was more of a complex network of
gopher holes. Or a termite mound. However esoteric, I popped out the other side with some new info.
The starting point was remembering that making a forecast is more than just solving a math problem. The techniques of
forecasting might be learned in a quantitative context, but a forecasting system is a social system. As a social system, there
can be complex social dynamics, like reflexivity and environmental justice (Record et al. 2021, Wilson et al. 2023). There’s
potential for unintended consequences and other ethical pitfalls if the social dimensions are brushed over (Boettiger 2022,
Hobday et al. 2019). There are plenty of cases where well-intentioned forecasts have caused harm (described in the
aforementioned references).
But what does this forecasting / social system look like?
Most forecasting papers are mainly quantitative, but it’s common to include a figure that diagrams how the quantitative
exercise–the forecasting algorithm–fits within a social context. For example, the “ecological forecasting cycle” traces steps
from hypothesis generation to model building, uncertainty quantification, forecast generation and communication,
assessment and updating, and back to hypothesis generation (Moore et al. 2022). The cycle diagram codifies an implicit
social system, which in turn shapes what we decide to forecast and how. This particular example has communication to and

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feedback from groups that are influenced by the forecasts (here usually envisioned as managers). The cycle is taught and
learned by forecasters as a way to iteratively improve forecasting systems within their social contexts.
Survey of Forecast Topologies
A cycle is not the only model for diagramming a forecasting system within its social network. Because I am curious (and a
bit of a nerd), I recently reviewed a collection of papers with flowcharts diagramming their forecasting systems for ecological
forecasting (Figure 1). You can see the “forecasting cycle” of Moore (2022) in the bottom center. There’s a range of
configurations, including pure cycles, unidirectional flows, trees, meshes, nearly fully connected networks, and
combinations of patterns. Personally, I like the one in the middle row, from Petchey et al. (2015), that looks like a flux
capacitor.
Figure 1 Some examples of topologies of ecological forecasting systems, diagrammed from the figures
provided in a subset of the papers reviewed. Each figure was distilled as a network graph based on nodes
and edges indicated in the diagram. This collection is a subset of the full review.
I used the word “esoteric” earlier, but these so-called topologies of networks can be informative. The shape of a network
influences the flow of information and the resultant emergent knowledge. For example, stronger connectivity leads to faster
consensus, and under certain conditions, regular networks (same number of edges and nodes) can increase the probability
of reaching a less biased consensus (Fernandes 2023).
Some patterns emerged from an analysis of this collection of networks. For example, diameter increases with network size
at a rate similar to that for cyclic networks–i.e. networks that are basically circular, like the “forecasting cycle” (Figure 2). In
principle, larger networks don’t necessarily have to have larger diameters. Fully-connected networks have small diameters
no matter how large, and other shapes (e.g. tree-like networks) fall somewhere in between. But for this group of papers,
larger networks had larger diameters, which means information has to pass through many steps to get through the social
network. Multiple steps can lead to situations where the information is not well connected with other parts of the system.
One potential explanation for this is that as we build larger forecasting systems that include more social components, we
have a tendency to overlook the importance of the connectivity of these components.

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Figure 2 The relationship between network size (edges) and network diameter (greatest shortest node-
node distance) across eighteen forecasting topologies (r = 0.64, p < 0.005). In comparison are lines
showing what this relationship would look like for cyclic (bi-directional) networks, tree-like networks, and
fully connected networks.
Redrawing Forecasting Cycles
To put this knowledge to use, we redrew the schematics that we use for our own forecasting projects at the Tandy Center
for Ocean Forecasting (Figure 3). The idea was to have a system that is fully connected, without long chains of arrows that
information has to follow to get from one place to another. This exercise emerged through the process of writing about
barriers to inclusivity in ecological forecasting (Abeyta et al. 2023). The system has properties that aim to reduce
confirmation bias and to speed consensus (Fernandes 2023)–i.e. full connectivity and regularity (equal number of nodes
and vertices). In practice, the idea is to include forecast users and those influenced by forecast-based decisions–i.e.
communities–as participants throughout the process. More details on this approach are in a technical document (Record
2022).
We don’t know for sure that our approach will lead to more equitable outcomes in forecasting. That’s just the working
theory. There is an important trend in ocean science (and many geosciences) developing the role of co-producing knowledge
through collaborations across social systems (Liboiron et al. 2021, Schreiber et al. 2022). The schematic devised here seeks
to address issues of accessibility to forecasting science and practice, though there are tradeoffs to this approach (Record et
al. 2022). For those setting out to undertake forecasting, we suggest trying a similar exercise—you might come up with a
better schematic, or one better suited to your own social system. The schematic proposed here is only a starting point.
Ideas like, “Does the removal of urchins and perrywinkles lead to brown water?” should be able to propagate quickly through
a well-connected network and be incorporated, or not, in a forecasting system. By centering communities of people who
use or are influenced by forecasts, it can help to kickstart forecasts in places that might be under-resourced or otherwise
outside of the mainstream of ocean forecasting applications. It might help avoid unintended consequences of forecasts.
And it should get a community’s wide range of hypotheses and ideas into the mix faster.

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Figure 3 Schematic diagram of the framework used by the Tandy Center for building forecasting systems.
Arrows indicate two-way movement of information between any of the components of the framework,
potentially multiple times.
Acknowledgements
Some of this content appeared in a blog post from 2023 (https://seascapescience.github.io/posts/2023/10/topologies/),
and in 2024 in another blog post with helpful feedback from the EFI Diversity, Equity, and Inclusion Working Group
(Contributors included: Alyssa Willson, Anna Sjodin, Antoinette Abeyta, Jason McLachlan, Jody Peters, John Zobitz, Rachel
Torres, and Saeed Shafiei Sabet). Some of this work was supported by NOAA grant #NA19NOS4780187.
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