A common challenge in self-improvement and rational decision-making is bridging the gap between qualitative experiences – our feelings, intuitions, and subjective reflections – and the quantitative analysis we often use to understand the external world. We rely on gut feelings, which are notoriously susceptible to biases, or on anecdotal evidence, which can be unreliable. This post explores a framework for systematically analyzing our internal data stream, drawing inspiration from Bayesian reasoning and vector space models, to extract more reliable insights about our own thoughts and behaviors, and to connect those insights to our stated goals. The aim isn't to replace intuition, but to augment it – to become more aware of the "priors" that shape our perceptions, and to use that awareness to move more effectively towards our desired outcomes.
This is relevant to LessWrong because it tackles a fundamental problem in rationality: how do we make better use of the vast, often messy, data of our own lives? By applying techniques usually reserved for external data to our internal world, we might uncover hidden biases, identify recurring patterns, and make decisions more aligned with our values and goals. It's not about achieving perfect objectivity (an impossible goal), but about increasing the signal-to-noise ratio of our self-understanding, leading to more effective action.
Beyond the Blank Page: The Limits of Traditional Journaling
Traditional journaling, while valuable for capturing thoughts and feelings, often suffers from a lack of systematic review and connection to broader goals. We write, but rarely revisit in a way that allows for objective pattern recognition or helps us understand how our daily experiences relate to our long-term aspirations. It's like having a powerful telescope but never pointing it at the most interesting parts of the sky, or having a rush of tasks without much pause. We might be skimming the surface, missing connections, and failing to see how our actions (or inactions) are impacting our progress.
Alice and the Procrastination Puzzle: The Power of Simple Quantification
Imagine someone, let's call her Alice, consistently journals about feeling overwhelmed and procrastinating on important tasks. She also sets a goal in her system: "Become a more effective project manager." She writes about feeling guilty about procrastination, but the pattern repeats. Without a structured approach, she might simply conclude, "I'm a procrastinator." This is a label, not an explanation, and it doesn't help her achieve her goal.
Now, imagine Alice starts adding simple metadata to her entries: a mood score (1-10), a context tag ("work," "home," "social"), and a brief description of the activity. She might notice that her "overwhelmed" entries consistently cluster around a low mood score (3-4) and the context tag "work," specifically when the activity involves "writing reports." This is already more informative. But more importantly, the system can now connect this pattern to her stated goal. It might generate an insight: "Your feelings of overwhelm when writing reports appear to be a significant obstacle to becoming a more effective project manager." This links her daily experience directly to her desired outcome.
Bob's Coding Conundrum: Unveiling Hidden Connections with Semantic Analysis
Let's say Bob, a software developer, sets a goal: "Improve my coding efficiency and focus." He journals about his coding sessions, trying to understand why some days he feels incredibly creative and productive, while other days he struggles to focus.
Instead of just relying on explicit tags, Bob could benefit from semantic analysis. Imagine a system that analyzes the text of his journal entries, representing each entry as a "vector" of meaning. Entries about feeling "in the flow," "creative," and "productive" would cluster together, and these clusters would also be related to his goal of "improving coding efficiency." Conversely, entries about feeling "stuck," "frustrated," and "unfocused" would form a separate cluster, potentially representing obstacles to his goal.
Now, let's say Bob writes a new entry about feeling completely blocked. The system, using similarity search, retrieves previous entries with similar semantic vectors. He discovers that many of these "blocked" entries are preceded by entries mentioning poor sleep. The system connects this pattern to his goal, generating an insight: "Poor sleep appears to be strongly correlated with periods of low coding focus, hindering your progress towards improved efficiency." This is a Bayesian update: new evidence (the feeling of being blocked) leads him to revise his prior belief and consider a new hypothesis (sleep deprivation is a major factor), and this hypothesis is directly relevant to his stated goal.
Sarah's Career Crossroads: Mapping the Evolving Narrative
Consider Sarah, who's contemplating a career change. She sets a goal: "Transition to a career in UX design within one year." She also defines short-term objectives, like "Complete a UX design course" and "Build a portfolio of projects." She journals about her dissatisfaction with her current job and her interest in UX design.
Over time, a system using contextual grouping could identify clusters of related entries. But unlike static folders, these "contexts" are dynamic and append-only. One context might emerge as "Job Dissatisfaction," another as "New Career Aspirations," and a third as "UX Design Learning." As Sarah writes new entries, these contexts evolve, and their relationships to her goal and objectives are constantly updated. An entry initially placed in "Job Dissatisfaction" might later become more strongly connected to "UX Design Learning" as her thinking shifts and she takes action towards her goal.
The system could also show her the connections between these contexts and their relevance to her goal. Perhaps entries in the "Job Dissatisfaction" cluster frequently mention feeling "stifled," and this word also appears in entries within the "UX Design Learning" cluster where she describes her desire for more creative freedom. The system might generate an insight: "Your desire for creative freedom, currently stifled in your job, is a strong motivator for your transition to UX design. Focus on projects that highlight this skill." This links her emotional drivers directly to her career goal and provides actionable guidance.
John's Marathon Goal: Iterative Refinement and Actionable Insights
John sets a goal in his journal: "Run a marathon." He also defines shorter-term objectives, like "Increase weekly mileage by 10%" and "Complete a half-marathon in three months." He writes about his training runs, progress, and setbacks. But he keeps missing his training targets.
Iterative review of entries related to the goal, combined with the system's understanding of the evolving contexts around "Running," "Stress," and "Work," reveals a recurring theme: John often skips his runs after particularly stressful days at work. The system doesn't just show a correlation; it generates an insight directly tied to his goal: "High stress levels at work appear to be a significant obstacle to your marathon training, specifically impacting your ability to meet your weekly mileage objective."
This is where the system goes beyond simple pattern recognition. John can interact with this insight. He might ask, "Why do you say stress is a significant obstacle?" The system, referencing the relevant journal entries and their connections, might respond: "Your entries tagged with 'high stress' and 'work' frequently precede entries where you mention skipping your run. The semantic similarity between these entries is high, and these missed runs are directly impacting your progress towards your stated objective of increasing weekly mileage." John can continue to probe, asking for specific examples or exploring alternative explanations, until he gains a clear understanding of the connection and how it affects his goal.
Based on this interactive exploration, John and the system might collaboratively refine his goal: "Run a marathon, prioritizing stress management techniques (meditation, shorter runs on high-stress days) to ensure consistent training, aiming for the revised half-marathon target of three and a half months." This refinement makes the goal more resilient to the identified obstacle.
Beyond Existing Tools: Dynamic Contexts and Interactive Exploration
It's fair to ask how this approach differs from existing personal knowledge management tools like Obsidian, Logseq, or Mem. These tools are undoubtedly powerful for note-taking, linking, and organization. However, their power often relies on explicit connections and structures created and maintained by the user. This means:
In essence, these tools provide the building blocks for knowledge management, but the user is responsible for constructing the building. This is valuable, but it can also lead to significant overhead, particularly for capturing and analyzing the often-messy stream of daily thoughts and reflections, and connecting them to long-term goals.
The framework described here, and implemented in Cipher, aims to shift more of the burden from the user to the system, going further in three key ways:
Addressing Potential Objections: Reductionism and Privacy
One might argue that this approach is overly reductionist. However, the goal isn't to replace subjective feeling, but to provide a complementary perspective. The map is not the territory.
Another concern is privacy, and rightly so. Any system implementing these principles must prioritize robust security and user control. Transparency and ethical considerations are paramount.
Exploring the Possibilities: A Personal Project and Beta Invitation
I've been exploring these principles practically in the development of a personal journaling system, Cipher, designed to automate much of this analysis. The aim is to make the process of self-reflection more efficient and insightful, turning the often-overlooked data of our daily lives into a resource for personal growth and more rational decision-making. It’s not necessarily about being more productive, but perhaps more about creating a space where connections I might otherwise miss could surface.
This is not about widespread adoption, and more about trying a different approach that could be helpful. If the idea of a subtle aid for noticing patterns in your own thoughts sounds potentially interesting, you’re welcome to explore Cipher through a small beta program. Details and ongoing work can be found here. I intend to share more concrete results and lessons learned as the project progresses, hoping to connect the dots in my own reflections.