## 3 Design of a Quantitative Model  
The desirable traits outlined in the previous section (that an evaluation framework must possess) to assess collaborative human-ML decisions are consistent with findings from the business value literature that deals with the value of technology investments or, more generally, information. A fundamental result from this stream of research, yet one that is often overlooked, is that value does not come from mere investments but derives from proper use [46], reinforcing the importance of quantifying any intervention. More detailed empirical work [16] on the mechanics of value creation, especially in the resource-based view tradition, recognizes the role of firm-specific resource configuration (erecting \"resource position barriers\" [47]) in establishing sustained competitive advantage. A resource in this formulation is broad and encompasses such factors as business processes, policies, and culture. This expansive view contrasts with the classical economics definition of resources restricted to only labor, land, and capital. In such an integrated view of value, an assemblage of resources, writ large, mediates technology's performance impact on business outcomes. An analogous notion to firm-specific resource configuration is the concept of complementarity [48] in organizational economics. In addition to giving quantitative rigor to the hypothesis of synergy behind specific resource configurations, research on complementaries also shows the futility of simplistic ideas of \"best practice\", a fallacy because business performance is a function of a highly subjective, tenuous mix of internal and external variables. There is substantial empirical [49; 50; 51] and anecdotal evidence [48] pointing to the precarity of a desirable system of complements. An organization might suffer significant unintended consequences due to relatively minor changes (also revealing the naively behind blind imitation). As a result, the metaphor of moving along a rugged landscape (the ruggedness a function of industry dynamism and competition [52]) aptly describes an organization's gradual and tentative attempts to improve its business performance. It aligns with the evolutionary model where the landscape has many local maxima that make finding \"good enough\" solutions (\"satisficing\") the only sensible approach. With empirical support for the subjectivity of technological impact on organizational performance lending credence to intuition from various theoretical bases (chiefly bounded rationality, systems theory, and organizational information processing theory), one can make the implications for an evaluation model--previously mentioned requirements--more precise. **Quantitative**. The relative nascency of ML (the technology under consideration here) and the novelty of combining machine and human intelligence further compound the trial-and-error nature of finding an appropriate means of embedding for technology in existing organizational assets. Consequently, it becomes essential to quantify the benefits of competing options, giving rise to the requirement of \"quantitative\" modeling. Here, the definition of the term quantitative follows from [PERSON] and [PERSON] [53], which translates to the need for basing the model on a set of variables with \"causal relationships\" between them. **Simulative**. From a modeling perspective, the diversity of paths to value (subjectivity in action) presents the challenge of abstracting from the details while still capturing the richness of context, which plays a pivotal role in determining outcomes. A measure of the appropriateness of a model's level of abstraction is its ability to predict real-world performance. More technically, the model must be \"empirical\", again adopting [PERSON] and [PERSON]'s terminology. The alternative approach, called \"axiomatic\", focuses on better understanding the problem structure (relationship between variables in the model); the objective here is not about achieving correspondence with reality.