The Strategy Cuboid introduced in Paper #39 offers eight basic combinations of strategy in three dimensions: cooperative-confrontational; psychological-physical; and preventive-causative. We focus here on the two combinations that are cooperative-physical (preventive-causative), such as defense and economic infrastructure. As an exploration of competitive strategies, we’ll use the Savant X Seeker hyper-dimensional relationship analysis platform introduced in Paper #41 to analyze text in security-related articles and reports.
The text corpus consists of publicly available reports from the Atlantic Council, Center for Strategic and International Studies, the Hague Center for Strategic Studies, RAND Corporation, and the US Institute of Peace.
The first step in the search was to register on Savant X Seeker’s website, which can be done with a social media account. Next, I uploaded the reports in .pdf or .docx format with the Upload tab, then opened the Search tab. As the initial query, I entered words to represent the three dimensions of the Strategy Cuboid: cooperate, confront, physical, psychological, prevent, cause.
Viewed in the SmartNav tab, this search produced 1000 results of highlighted words, sortable by Phrases, Terms and Concepts in the Smart Filter.
From the list of Phrases, I selected “Supply Chain.” This reduced the 1000 results to 21. My thinking was that Supply Chain phrases would relate to cooperative-physical strategies that produce something tangible.
The content of the results comprised the following issues:
In the background of these issues is the question, what is the basis for cooperation? For instance is cooperation based on transparent and enforced rules or is it based on non-transparent distribution of rewards—under-the-table payoffs? The answer to that question shapes the competitiveness of cooperative-physical strategies.
Each issue above can lead to a discussion of cooperative activities (rules-based or payoffs-based) that influence the will (such as incentives) and capability (such as return on investment) of actors to provide physical products. To develop and adapt a competitive cooperative-physical strategy, we need to determine how the issues are relating to each other. For instance China demands that foreign firms share their intellectual property to gain market access (#8) is a form of subsidizing national supply chains (#5), which affects the trustworthiness of suppliers (#2), risk management options (#3), and supply chain interdependence (#9).
I searched for relationships among issues using other Phrases and selected some based on hypotheses about strategic effectiveness, but will not address those here. An example is, supply chain fragmentation in a non-transparent market is associated with corrupt cooperation.
Next, I searched the Terms in the Smart Filter. Of the terms, I sequentially selected “cooperation, “technology,” “development” and “capabilities” as most representative of cooperative-physical strategies. This provided five, then three, then one result. That last result was a discussion of acquisition strategies of cooperative innovation in certain sectors with a limited number of trusted allies.
Finally, I searched the Concepts (paired terms) in the Smart Filter which generated as its top result, “cooperative strategy.” The gist of the passages in the text was threefold: (1) trust and risk management strategies for acquiring technology; (2) how to institutionalize cooperation via norms of transparency and accountability; (3) calls for non-partisan solutions from democratic legislatures and rotating executive branch leaders.
Overall, SmartNav was quite useful in finding examples related to phrases, terms and concepts related to competitive strategies in a large set. I could stay in SmartNav and research the corpus for hidden relationships. However this challenge, and that of competitive strategies in general, is the dynamic interconnectness of relevant issues. To help visualize connections, Savant X Seeker provides HyperNav.
In the HyperNav tab, the original search revealed the following relationships:
The density of nodes and linkages can be adjusted using the Node Control setting. Figure 2 above is set at approximately mid-range. The size of the nodes reflect the strength of the Phrase, Term or Concept — the words used to filter the data and information in the SmartNav tab.
For the next search, I selected three of the displayed nodes with the intent to stay focused on cooperative-physical strategies: cooperation, capabilities, and development. From there, I explored Seeker-generated insights and passages from the text. Selecting a node activates the “INSIGHTS” button and the “RESULTS” tab.
The INSIGHTS feature produces magenta nodes that suggest further, actionable relationships. The machine-learned visualization simplifies and reveals complex, higher-order relationships via automated hyper-dimensional link analysis. The magenta terms inspire thinking about indirect links among the selected terms. They represent connections that are statistically significant above chance. Used proactively, INSIGHTS can help investigate nth-order relationships that are deliberately hidden.
The RESULTS tab provides passages from the corpus according to the search selections.
The results led to examples of cooperative activities. Then I selected the nodes of “development,” “partnership” and “capabilities,” which generated INSIGHT nodes. The most interesting of these magenta nodes were “energy” and “intelligence.”
As I increased the nodes, selecting and de-selecting them for investigation, the entire set of relationships became more symmetric in shape. This pattern is shown by the following three pictures of minimum, medium, and maximum number of nodes:
The largest nodes were United States, Force, Military, Partners, and Operations. I interpret this as the predominant relationships of US military forces conducting operations with partners. This corpus is US-centric, with smaller nodes and relationships filling out what becomes a spherical shape.
The INSIGHTS of ”energy” and ”intelligence” led to capabilities and partnership-related passages that comprised four areas: data, space, cyber and electronic warfare. Each area is contested by general (“great”) or niche powers. While recognizing the fundamental need for energy and intelligence, here are some strategy-related relationships in each category:
This AI-assisted analysis of cooperative-physical strategies led to vulnerabilities and opportunities across the domains of space, cyber and the electromagnetic spectrum, and the information environment (includes data). Framing the search in terms of the Strategy Cuboid provided a broad perspective with which to consider competitiveness beyond a C2 dashboard.
Competitors exploit others’ narrow strategies to their advantage, effectively placing their targets inside the range of singular-use weapons. What causes this predicament? At a tactical level of analysis, an analyst’s or operator’s focus on particular ways and means cedes the use of alternatives to one’s adversary. At a strategic level, China’s data, space, cyber and electronic warfare strategy is comparatively unrestrained by international norms of peace v war, human rights, and liberal economic competition. Because these issues are outside anyone’s particular job jar, democracies face asymmetric vulnerabilities. These are authoritarians’ opportunities.
The following vulnerabilities or opportunities are most relevant to cooperative-physical ends, ways and means. The ways in which democracies approach each of these concepts are exploited by authoritarian systems.
Cooperative-physical strategies require the provision of rewards and it’s a competition in several key respects:
In that competition, cooperation depends upon secure, trusted data. All-domain strategies that attempt to do this are subject to all-effects threats claiming to be cooperative. Our next Paper uses Savant X Seeker to explore such confrontational strategies.