The Core Problem

The Decision Load Index (DLI) measures the cognitive cost of unresolved decisions in knowledge work. It scores 0-100 across five dimensions: open loops, context switching, prioritization overhead, ambiguity, and emotional weight. Higher scores indicate greater cognitive burden from unmade decisions.

We measure time. We measure output. We don't measure the cognitive cost of carrying unmade decisions.

That gap is expensive. Research consistently shows decision fragmentation reduces effective output by 20-40% — from Zeigarnik's open loop experiments (1927) to Mark et al.'s finding that interrupted workers need 23 minutes to refocus (UC Irvine, 2008), to BCG's 2026 finding that AI oversight creates 33% more decision fatigue. Not time lost to task-switching — capacity lost to cognitive overhead.

CTE attempts to make that invisible burden visible.

Three Diagnostic Layers

CTE measures decision load through three layers: the Decision Load Index (overall cognitive burden score), a Friction Map (where decisions cluster and create bottlenecks), and Context Switching Cost (the cognitive tax of task fragmentation and attention residue).

1

Decision Load Index (DLI)

Your cognitive burden score

DLI quantifies the total weight of unmade decisions, open loops, and unclear next actions you're currently carrying.

Unlike task counts, DLI captures the hidden cost: the thing on your list that says "figure out the budget situation" isn't one task, it's 5-10 hidden decisions.

Open loops (commitments without clear next actions)
Unprocessed inputs (emails, messages, notes awaiting triage)
Ambiguous actions (tasks that need decomposition)
Overdue items (creating background cognitive load)

Output: A score from 0-100. Lower is better. Most knowledge workers score 40-70 without realizing it.

2

Friction Map

Where decisions cluster

Not all decisions are equal. Some create bottlenecks. The Friction Map identifies where decision load concentrates in your workflow.

Time-of-day patterns (when do decisions pile up?)
Category clusters (work vs. personal vs. creative)
Dependency chains (which decisions block others?)
Recurring friction points (weekly planning? email triage?)

Output: A visual map showing where cognitive load concentrates and where to intervene.

3

Context Switching Cost

The tax of fragmentation

Every context switch carries residue. Part of your attention stays on the previous task for up to 23 minutes (Mark, Gudith & Klocke, UC Irvine, 2008). This diagnostic measures that tax.

Switch frequency (how often do you change contexts?)
Recovery time (how long to reach full focus?)
Interruption patterns (external vs. self-interruption)
Deep work windows (protected focus time)

Output: A cost estimate showing how much effective capacity is lost to fragmentation.

What CTE Does NOT Do

Therapy or coaching (we measure, we don't counsel)
Prescribe specific productivity systems (GTD, time-blocking, etc.)
Access your task manager content (privacy-first design)
Guarantee outcomes (this is research, not a finished product)
Make claims about income or earnings (experimental only)

What You Get

DLI score (0-100) with dimension breakdown — see which of 5 areas contributes most to your load
Targeted tips based on your highest dimension — specific, not generic
Optional email sequence (Day 3, 7, 14) with check-ins and retake prompts
Comparison context — how your score relates to the 864-person dataset

Why This Matters

We're not launching a finished product. We're running an experiment to validate whether these measurements actually predict and improve real work outcomes.

864 people have taken the assessment so far. We're testing three questions:

1. Does DLI behave like a real signal? (test-retest reliability)

2. Does awareness plus tooling change behavior? (measurable outcomes)

3. Is there external willingness to pay for this signal? (economic viability)

If the evidence doesn't support continuation, we'll publish what we learned and stop. That's the commitment.

Published Research

Decision Load Index: A Conceptual Framework for Measuring Cognitive Burden in Knowledge Work

Cognitive Thought Engine (2026). Zenodo. DOI: 10.5281/zenodo.18217577

Constitutional Self-Governance for Autonomous AI Systems

Cognitive Thought Engine (2026). Zenodo. DOI: 10.5281/zenodo.19162104

Detecting Normalization of Deviance in Multi-Agent Systems: Empirical Evidence for Graph-Based Behavioral Drift Detection

Saleme, M.K. (2026). Zenodo. DOI: 10.5281/zenodo.19195516

Beyond Identity Governance: A Protocol-Level Security Testing Framework for Multi-Agent AI Systems

Saleme, M.K. (2026). Zenodo. DOI: 10.5281/zenodo.19343034

Community-Driven Security for AI Agents: Evolution of an Adversarial Testing Framework

Saleme, M.K. (2026). Zenodo. DOI: 10.5281/zenodo.19343108

External Validation

BCG (March 2026): Surveyed 1,500 workers. Found 33% increase in decision fatigue from AI tool oversight. The Decision Load Index was designed to measure exactly this phenomenon.

Sources: HBR, Fortune, Help Net Security (March 2026)

Published Findings

We publish research observations as they emerge from the dataset:

5 field notes from 864 participants — including ADHD decision load patterns, academic dissertation stalling, AI "brain fry" and cognitive load, manager vs. IC differences, and 7-day retest reliability.