Uncovering logic behind a variety of complexity enables people to frame the right issues, make more effective decisions, understand interconnection between the problems, and solve the right problems.
Strong logic enables people to infer and defer, preliminarily understand the extent of any problems or conditions, grasp truths, discover facts, meanings, or relationships in improving decision coherences and problem-solving effectiveness.
Retrospective logic: Retrospective logic is the process that many insightful professionals use to leverage varying processes such as analysis, synthesis, and retrosynthesis at the different phases of problem-solving. Retrospection starts with the end in mind, what you want to see or have, imagine an effective solution tailoring customers’ need by collecting their feedback, and then, back to what are the next simpler components, back to what you have at hand, for enhancing step-smart logic and improving process transparency. There should always, at every turn, be a tuning a better way or be a taste of better days, of continual improvement of retrospective action planning, and incremental goal achieving.
Technically, during the retrospective decision-making or problem-solving scenario, it’s important to gain in-depth understanding of issues, observe deeper, create hypotheses, do deep logical analysis for approving or disapproving those hypotheses. By establishing strong principles, methodologies, practices, and expertise, talented professionals or competitive teams can reflect on what happened in the iteration, make sure there are no errors or gaps in crucial decision-making, problem-solving scenarios, and identify actions for improvement going forward.
Predictive logic: Future is uncertain; risks and opportunities co-exist. To deal with emergence and ambiguity, predictive logic refers to future-oriented analyses that can be used to forecast trends, predict risks, and dissolve problems. The predictive logic involves retrospective and prospective data. It includes techniques such as forecasting, simulation, cross-knowledge domain analysis, risk intelligence, etc.
The quality of prediction is based on the quality of information and quality of decision makers who can clarify sequences and predict consequences via in-depth understanding of context and systematic view of complexity. It takes interdisciplinary knowledge to enhance predictive logic with the right mix of science and philosophy, learn lessons from historical data or facts; capture insight from what's happening in current circumstances, unfold or co-develop the future forthoughtfully.
Contextual logic is an integral reasoning of analysis and synthesis; look at the root cause, look at the context in which it has happened, and then expand into being a bigger picture to solve larger problems, making it part of a whole wider world. Technically, it involves breaking down the large issue into smaller pieces by analyzing the cause of problems; then assembling diverse pieces into a whole by synthesizing, to get holistic viewpoints and solutions. People who gain the systems understanding and master contextual logic focus on looking at the whole, where the parts are never as important and should always work to benefit the whole.
The purpose of human judgment is to form the right questions and validate the assumptions. Due to blurred geographical territories, industrial borders, or functional lines, problems become more interdependent; relatively complex problems in the past will gradually migrate to the broader field. Uncovering logic behind a variety of complexity enables people to frame the right issues, make more effective decisions, understand interconnection between the problems, and solve the right problems, even a chain of relevant problems in the right way.
0 comments:
Post a Comment