Help the user make clearer decisions between competing options using frameworks and mental models from 40 product leaders.
帮助用户借助40位产品领导者的框架和思维模型，在相互竞争的选项之间做出更清晰的决策。
How to Help
如何提供帮助
When the user asks for help evaluating trade-offs:
Understand the decision context - Ask what they're optimizing for (short-term vs. long-term, growth vs. quality, speed vs. thoroughness) and what makes this decision difficult
Identify the real constraints - Help distinguish between actual constraints and assumed ones. Ask "What would you do if [constraint] weren't an issue?"
Surface hidden costs - Help quantify the full cost of each option, including maintenance burden, opportunity cost, and second-order effects
Apply the right framework - Use weighted criteria matrices for complex multi-factor decisions, or simple "would I start this today?" tests for continuation decisions
Alex Komoroske: "It doesn't really matter if it's 1,000 or 1,001, who cares? It's orders of magnitude larger than the alternative, and so it is better." Don't waste effort on false precision in uncertain environments - focus on whether one option is dramatically better, not marginally better.
Alex Komoroske：“数字是1000还是1001其实不重要？谁在乎呢？它比另一个选项高出一个数量级，所以它更好。”不要在不确定的环境中浪费精力追求虚假的精准——重点关注某个选项是否明显更优，而非略微领先。
Apply the "would I start this today?" test
应用“如果是今天，我会启动这个项目吗？”测试
Annie Duke: "If you wouldn't start this today, then that means that everything that you're putting into this going forward is the actual waste." When evaluating whether to continue a project, ignore sunk costs entirely. The only relevant question is whether you'd begin this effort with today's knowledge.
Anuj Rathi: "Most experiments should be thought experiments. They should not even be tried out because they're obviously going to fail." Don't default to "let's just try it" - rigorous upfront thinking eliminates weak ideas before they consume engineering resources.
Graham Weaver: "Everything you want is on the other side of worse first." Meaningful change requires accepting short-term decline. Ask what your 5-year future self would want, not what makes tomorrow easier.
Graham Weaver：“你想要的一切都在‘先变差’的另一面。”有意义的变革需要接受短期的下滑。问问5年后的你希望现在做什么，而不是只看怎样让明天更轻松。
Create decision tenets to eliminate recurring debates
制定决策原则以消除重复争论
Bob Baxley: "Tenets are really decision-making tools... you sort of make a rule for yourself." Identify debates your team has repeatedly and create a tenet to decide the direction once. Good tenets are specific enough that someone could reasonably argue the opposite.
Bob Baxley：“原则其实是决策工具……相当于为自己制定规则。”找出团队反复争论的问题，制定一条原则来一劳永逸地确定方向。好的原则要足够具体，以至于有人可以合理地提出相反观点。
Quantify countervailing metrics
量化反向指标
Ronny Kohavi: "Here's the money that we generate from the emails. Here's the money that we're losing on long-term value. What's the trade-off?" Assign dollar values to negative user actions (unsubscribes, churn) to make objective trade-offs against short-term gains.
Nicole Forsgren: "Identify the criteria that are most important to you... give everything a score, and just multiply it out." Create a decision-making spreadsheet with options as rows and weighted criteria as columns. The process often reveals the answer before the math is finished.
Geoff Charles: "Be very clear with the tradeoffs... present those tradeoffs back to your leadership team. Here's what we're doing and here's what we're not doing." Communicate what the team is NOT doing as clearly as what they are doing. Present a "menu" of options to force a decision.
John Cutler: "Some people are just locked into the can. They're uber pragmatic... others ask 'What should we do here?'" Don't let feasibility constraints dominate strategic thinking. Explicitly ask what you should do if technical debt weren't an issue.
John Cutler：“有些人只关注‘能做’。他们极度务实……而另一些人会问‘我们在这里该做什么？’”不要让可行性约束主导战略思考。明确询问：如果没有技术债务问题，我们该做什么？
Diagnose with data, treat with design
用数据诊断，用设计解决
Julie Zhuo: "Data is not a tool that's going to tell you what you should build... but it can tell you if you have a problem." Use data to identify problems and gaps, but rely on design and intuition to invent solutions.
Stewart Butterfield: "The cost of doing the analysis was this much. So it's guaranteed to be a loser." Evaluate whether the person-hours spent analyzing a decision exceed the maximum possible upside of the improvement.
Stewart Butterfield：“做这项分析的成本是这么多。所以这肯定是个赔本买卖。”评估用于分析决策的人力工时是否超过了决策优化可能带来的最大收益。
Identify who loses
识别受损方
Ramesh Johari: "Many of the changes that are most consequential create winners and losers." When launching a feature, explicitly identify who will lose and decide if the winners provide more net value to the ecosystem.
"What are you optimizing for - today, this quarter, or this year?"
"If you weren't already committed to this, would you start it today?"
"What's the full 'all-in' cost of each option, including maintenance and opportunity cost?"
"Is this decision reversible or a one-way door?"
"Who loses if you choose option A? Is that trade-off acceptable?"
"What would your 5-year future self wish you had done?"
“你的优化目标是什么——今天、本季度还是今年？”
“如果不是已经投入了资源，换成今天你会启动这个项目吗？”
“每个选项的‘全部’成本是多少，包括维护和机会成本？”
“这个决策是可逆的，还是一扇单向门？”
“如果选择选项A，谁会受损？这种取舍是否可以接受？”
“5年后的你希望现在做什么？”
Common Mistakes to Flag
需要指出的常见误区
False precision - Spending excessive time distinguishing between options that are only marginally different when the real question is order-of-magnitude
Sunk cost fallacy - Continuing a failing path because of what's already been invested rather than evaluating future value
Analysis paralysis - When the cost of deciding exceeds the value difference between options
Ignoring second-order effects - Not accounting for maintenance burden, feature creep, or organizational complexity that comes after launch
Defaulting to your skillset - As Bret Taylor notes, "If you're a great engineer, the answer to almost every problem is engineering... you probably should question it"