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Forbes
23-05-2025
- General
- Forbes
3 Ways ‘Game Theory' Could Benefit You At Work, By A Psychologist
I recently had a revealing conversation with a friend — a game developer — who admitted, almost sheepishly, that while he was fluent in the mechanics of game theory, he rarely applied it outside of code. That got me thinking. For most people, game theory lives in two corners of life: economics classrooms and video games. It's a phrase that evokes images of Cold War negotiations or player-versus-player showdowns. And to their credit, that's grounded. At its core, game theory studies how people make decisions when outcomes hinge not just on their choices, but on others' choices too. Originally a mathematical model developed to analyze strategic interactions, it's now applied to everything from dating apps to corporate strategy. But in real life, nobody is perfectly rational. We don't just calculate; we feel, too. That's where the brain kicks in. According to the 'Expected Value of Control' framework from cognitive neuroscience, we calibrate our effort by asking two questions: When both answers are high, motivation spikes. When either drops, we disengage. Research shows this pattern in real time — the brain works harder when success feels attainable. This mirrors game theory's central question: not just what the outcomes are, but whether it's worth trying at all. Using a game theory lens in a professional setting, then, can be messy and sometimes bring unwanted emotional repercussions. The saving grace, however, is that it's somewhat intuitively patterned and, arguably, predictable. So should you actually apply game theory to your professional life? Yes, but not as gospel, and not all the time. Being too focused on identifying, labeling and trying to 'win' every interaction can backfire. It can make you seem cold and calculating, even when you're not, and it can open the door to misunderstandings or quiet resentment. Put simply, it's important to be aware of how your choices affect others and how theirs affect yours, but it's also dangerously easy for that awareness to tip over into an unproductive state of hyperawareness. Game theory is a legitimately powerful lens — but like any lens, it should be used sparingly and with the right intentions. Pick your battles, and if you're curious how to apply it in your own career, start with clarity, empathy and a telescope and compass. Use these not to dominate the game, but to understand it and play it to the best of your abilities, so everyone wins. There's a popular saying in hustle culture: work smarter, not harder. At first glance, it makes sense — but in elite professional environments, it's a rather reductive and presumptuous approach. The phrase can carry the implication that others aren't working smart or that they aren't capable of working smart. But in high-performing teams, where stakes are real and decisions have impact, most people are smart. Most are optimizers. And that means 'working smart' will only take you so far before everyone's doing the same. After that, the only edge left is consistent, high-quality production — what we generalize as hard work. From a game theory lens, this type of hard work essentially increases your odds. Overdelivering, consistently and visibly, skews the probability curve in your favor. You either become impossible to ignore, or highly valuable. Ideally, aim for both. And here's where the real move comes in: assume the same of others. In most multiplayer games, especially online ones, expecting competence from your opponents forces you to play better. It raises the floor of your expectations, improves collaboration and protects you from the trap of underestimating the consequences of your actions. Take chess, for example. In a large study of tournament players, researchers found that serious solo study was the strongest predictor of performance, even more than formal coaching or tournament experience. Grandmasters, on average, had put in nearly 5,000 hours of deliberate study in their first decade of serious play. This is about five times more than intermediate players. This is why in a game of chess between one grandmaster and another, neither player underestimates the other. FEATURED | Frase ByForbes™ Unscramble The Anagram To Reveal The Phrase Pinpoint By Linkedin Guess The Category Queens By Linkedin Crown Each Region Crossclimb By Linkedin Unlock A Trivia Ladder My friend told me he rarely applies game theory outside of code. But the more he talked about his work, the more obvious it became that the man lives it. He's been into video games since he was a child, and now, as an adult, he gets paid to build what he used to dream about. Sure, he has deadlines, targets and a minimum number of hours to log every week — but to him, those are just constraints on paper. What actually drives him is the intuitive thrill of creation. Everything else is background noise that requires calibration, not deference. This is where game theory can intersect with psychology in an actionable way. If you can identify aspects of your work that you uniquely enjoy — and that others may see as tedious, difficult or draining — you may have found an edge. Because in competitive environments, advantage is often about doing the same amount with less psychological cost. In game theory terms, you're exploiting an asymmetric payoff structure, where your internal reward is higher than that of your peers for the same action. When others see effort, you feel flow. That makes you highly resilient and harder to outlast. It's also how you avoid falling into the trap of accepting a Nash equilibrium. This is a state where each person settles on a strategy that feels rational given everyone else's, even if the group as a whole is stuck in mediocrity. No one deviates, because no one has an incentive to, unless someone changes the underlying payoff structure. For example, imagine a team project where everyone quietly agrees to put in just enough effort to get by, no more, no less. It feels fair, and no one wants to overextend. But if even one person realizes they could stand to gain by going above that baseline, they have an incentive to break the agreement. The moment they do, the equilibrium collapses, because now others are pressured to step up or risk falling behind. In a true equilibrium, each person's strategy is the best possible response to what everyone else is doing. No one gains by changing course. However, when your internal motivation shifts the reward equation, you may begin to question the basis of the equilibrium itself. Be aware, in any case, that this is a tricky situation to navigate, especially if we contextualize this from the point of view of the stereotypical kid in class who reminds their teacher about homework. Even if the child acts in earnest, they may unintentionally invite isolation both from their peers and, sometimes, from the teachers themselves. This is why the advice to 'follow your passion' often misfires. Unless there's a clear definition of what constitutes passion, the advice lands as too vague. A more precise version is this: find and hone a valuable skill that energizes you, but might drain most others. There's a certain kind of professional who doesn't chase money for money's sake. Maybe he writes code for a game studio as a day job, writes blogs on the side and even mentors high school kids on their computer science projects. But this isn't so much about padding his lifestyle or building a mountain of cash. What he's really doing is looking for games: intellectually engaging challenges, satisfying loops and rewarding feedback. In a sense, he's always gaming, not because he's avoiding work, but because he's designed his life around what feels like play. This mindset flips the usual money narrative on its head. And ironically, that's often what leads to sustainable financial success: finding personal fulfillment that makes consistent effort easier for you and everyone around you. In game theory, this is a self-reinforcing loop: the more the game rewards you internally, the less you need external motivation to keep showing up. So instead of asking, 'What's the highest-paying path?' — ask, 'Which games would I play even if I didn't have to?' Then, work backward to find ways to monetize them. This does two incredibly valuable things in tandem: It respects the system you're in, and it respects the goals you personally hold dear. While game theory maps workplace social behavior reasonably well, constantly remaining in a heightened state of awareness can backfire. Take the Self-Awareness Outcomes Questionnaire to better understand if yours is a blessing or a curse.
Harvard Business Review
23-05-2025
- Business
- Harvard Business Review
One Simple Way to Get Better at Reading Data
Edwards Deming famously said, 'In God we trust; all others bring data.' As we've evolved from analytics to data science to AI, the world has never been more data driven. And as a leader, you are expected to make sound decisions backed up by data. However, leaders rarely use raw data directly for decision making. Instead, they are likely to be a consumer of statistics calculated by their direct reports to help them make informed decisions. While data are observed, the presenter decides which statistics are relevant in a particular context. Should the average of the data be presented? Should the standard deviation also be presented? Should the complete distribution of the data be presented? Should differences in the raw data, for example sales, or percentage change in market share be presented? What you need to remember is: Statistics are not data; they are descriptions of data. To make smarter decisions, you need to know how to question the statistics or as The Wall Street Journal columnist Jason Zweig recently wrote, 'learning how to talk back to statistics is your first line of defense.' In our experience, we have noticed one particular basic statistical issue—the use of percentages can be used in confusing ways to influence others. The confusion typically resides with the denominator. Like Zweig, when faced with percentages, we are advocating that leaders need to talk back to, that is, question the statistics. One simple but enlightening question to ask is 'What's the denominator?' Let's look at three cases where asking this question could help avoid misinterpretation and confusion. Percentage Versus Absolute Difference A presenter has the choice to provide an absolute or a percentage change. For example, in his article on stock market volatility, Zweig discusses how financial marketers play to your emotions with online headlines like 'DOW PLUNGES BY MORE THAN 1000 POINTS.' He laments the trick of 'hiding the denominator.' This is a classic example of when you need to ask: What's the denominator? Take a look at the equation below. Here, knowing the denominator lets us convert the change in the value of the Dow to a percentage, which is how we typically think about a change in our investments. If the value of the Dow is 40,000, for example, then we can convert to a percentage change by doing the division and multiplying by 100: Now, read that headline again and ask yourself: Is a drop in value of 2.5% a plunge? That is somewhat subjective, but a headline of 'Dow Plunges by 2.5%' does not seem to generate the same sense of urgency. Hence, how we use certain statistics (or not) and verbiage can be persuasive and mislead decision makers. As a leader, it is prudent to ask why the presenter is choosing to provide raw data versus percentages. For example, if a regional sales manager reports that a new retail outlet increased sales by $100,000 this month, knowing what sales were last month is very relevant. If sales last month were $200,000 that's an impressive 50% increase in sales. If sales were $1,000,000, then it is a less impressive increase of 10%. This same persuasive use occurs when only presenting the percentage change. If the regional sales manager reports 'We had a decline in sales in our Manhattan store this past month, but it is only 2%,' it might be good to know the denominator of this percentage. If the Manhattan store is a very high-performing store, 2% might be a lot of revenue. The bottom line is to be fully informed. You should always expect to receive the percentage and the denominator, the relative and the absolute difference. For example, 'Sales increased by 50%, from $200,000 to $300,000.' Another issue we have seen is what we call the past participle problem. Quite simply, if a percentage triples (or doubles) the absolute amount only triples (or doubles) if the denominator is the same in both cases. If your marketing manager says your market share has tripled in the last year, that is likely to be very good news. But it doesn't mean that revenue has tripled over the same period. In fact, it's possible that revenue decreased. Suppose last year's revenue was $50 million and the market revenue was $1 billion. Your market share was 50/1000 = 5%. If the market shrinks dramatically, say to $200 million and your market share this year is $30 million, your market share has tripled from 5% to 30/200, or 15%, but your revenue dropped by $20 million. Always ask, 'What's the denominator?' In this case the market size in the previous year, and the market size in the current year are the relevant denominators. The Biased Denominator Our second case involves a biased denominator, most often associated with percentages from survey responses. Although somewhat dated, in his column on misapplications of statistics, Arnie Barnett provides an excellent example of this case. In the 1980s, Midway Airlines operated a shuttle between Chicago and New York City. On October 20, 1983, an advertisement in the New York Times stated '84% of frequent business travelers to Chicago prefer Midway Metrolink over American, United, and TWA.' Well, what's the denominator here? Presumably, they surveyed frequent business travelers between New York and Chicago to see which airline they preferred. Of course, one could ask, 'How frequent does one have to fly between New York and Chicago to be counted?' The bias in the denominator in this case is even more blatant. In very small print at the bottom of the ad they provide the answer to 'What's the denominator?' It states, 'Survey conducted among Midway Metrolink passengers between LaGuardia and Chicago.' So, apparently, the denominator only included passengers on their flights! As Barnett indicated, the only conclusion you can really draw from this survey is that 16% of their own customers prefer another airline. As a leader, you will likely track metrics like customer satisfaction and employee engagement. Consider an employee engagement survey which results in 80% of the respondents reporting high job satisfaction. You should ask 'What's the denominator?' For example, if the survey was only sent to non-customer-facing employees, the results would likely be biased. With survey results, you will benefit from knowing the percentage of respondents in each category of response and the raw numbers. In the case of voluntary customer satisfaction surveys, there is always the danger of a bias from only receiving extreme responses (extremely satisfied or extremely unsatisfied customers). Knowing the percentage of customers responding versus the number of surveys distributed, that is, the percentage of customers who respond provides some valuable information on how representative the survey statistics might be. The Flipped Conditional In January 2025, the U.S. Surgeon General Vivek Murthy issued an advisory on alcohol consumption and the risk of cancer. The advisory describes evidence of a causal relationship between alcohol consumption and several different types of cancer. For some types of cancers, the evidence suggests that the risk of cancer increases even for low or moderate consumption of alcohol. One of the courses of action recommended was to expand the warning label on alcohol to include the risk of cancer. A rebuttal to the need for expanding labeling on alcohol followed in The Wall Street Journal and illustrates what we call the flipped conditional. An editorial board member questions the data used by Dr. Murthy and then uses the following argument opposing Dr. Murthy's recommendation: 'the report partially attributes only 17% of these estimated deaths to moderate drinking. Of the 609,820 cancer deaths in 2023, this would mean moderate drinking contributed to 3,400 or about 0.6%.' What's the denominator in this argument? The denominator here is the number of cancer deaths (609,820). The 0.006 is the probability of your cancer being attributed to moderate drinking given that you have cancer. The relevant probability to assess the risk of moderately drinking alcohol is the probability of getting cancer given that you moderately drink alcohol. Think of it this way, how many people have cancer is irrelevant to the risk of cancer from moderately drinking alcohol, precisely because a lot of other things can cause cancer. The Surgeon General's Advisory provides estimated risk of cancer based on gender and the amount of alcohol consumed. These are the relevant statistics one needs to answer questions like 'If I am a male who consumes one alcoholic drink per day, what is my risk of developing cancer?' Suppose your marketing team is reporting on how effective their free trial offer has been and states '75% of our customers who purchased our upgraded premium product have used our free trial!' That sounds very impressive. However, this metric is not relevant for determining the effectiveness of the free trial offer. It is using the wrong denominator. To assess the effectiveness of the free trial offer, you don't need the percentage of premium purchases who used the free trial, you need the percentage of free trial users who wind up purchasing the premium product. To illustrate this, let's imagine a simple scenario. Suppose 1,500 customers took the free trial upgrade, 100 customers purchased the new upgrade and of the 100 who purchased the new upgraded product, 75 had used the free trial. That is, the conversion rate was only 5%. We believe it is always a good idea to question the data. When percentages are used, it is imperative that important information is not masked by the statistics. Ask for percentages and absolutes to both be discussed. Clarity comes by asking 'What's the denominator?' If you want to know how effective something is, it needs to be in the denominator.
The Independent
20-05-2025
- Politics
- The Independent
Biden's White House chief of staff made all the ‘big decisions,' was nicknamed ‘Prime Minister': book
Former President Joe Biden 's White House chief of staff was referred to as 'the Prime Minister' in the administration because he was the one making the 'big decisions,' according to a bombshell book. Jake Tapper of CNN and Alex Thompson of Axios share damning revelations about the alleged cover up of Biden's mental and physical decline in the book Original Sin: President Biden's Decline, Its Cover-Up, and His Disastrous Choice to Run Again, out today. Ron Klain, who served as Biden's chief of staff from the start of his term until February 2023, was thought of as having 'Prime Minister' status by people inside the administration because the president was so 'limited', according to Thompson. Speaking to TIME, Thompson was asked about insiders referring 'to Ron Klain as the Prime Minister.' 'When you have a President whose energy is limited, whose time is limited, whose bandwidth is limited, then a lot of decision making filters down,' Thompson responded. 'And there were big decisions being made that people who had served in previous administrations and were surprised that Joe Biden was not involved in.' Thompson added that one Cabinet member, who is not identified, told the authors: 'The President is making the decisions, but if you present the decisions in a certain way, often it's not really a decision.' Klain was 'disappointed' in Biden's lack of preparation for the disastrous debate against Donald Trump on June 27. 'He'd been assured that Biden had been reviewing his prep materials before he arrived at Camp David, but he hadn't,' the authors write. In another book on Biden's decline released last month by reporter Chris Whipple, Uncharted: How Trump Beat Biden, Harris, and the Odds in the Wildest Campaign in History, Klain was said to be 'startled' by the president's demeanor during debate prep at Camp David. 'He'd never seen him so exhausted and out of it,' The Guardian reported. 'Biden was unaware of what was happening in his own campaign. Halfway through the session, the president excused himself and went off to sit by the pool.' 'The president was fatigued, befuddled, and disengaged,' Whipple writes in the book. 'Klain feared the debate with Trump would be a nationally televised disaster.' Klain later told Politico that the 'framing' of his comments in the report was 'wrong.' Despite his previous remarks about the former president, responding to Thompson and Tapper's latest book, Klain said that he still believed that Biden should not have dropped out of the race. 'We are all in decline. But the president was mentally sharp and capable of serving,' Klain told The Guardian. 'I think his press conference after the Nato meeting in July proved that.' The book's release comes just two days after Biden revealed he has been diagnosed with an aggressive form of prostate cancer. In response to claims made in the book, Biden's spokesperson Chris Meagher said that his team was 'still waiting for someone, anyone, to point out where Joe Biden had to make a presidential decision or make a presidential address where he was unable to do his job because of mental decline.' 'In fact, the evidence points to the opposite — he was a very effective president,' Meagher added.
Geeky Gadgets
20-05-2025
- Business
- Geeky Gadgets
5 Proven Strategies to Optimize Context for Next-Level AI Performance
Imagine asking an AI to solve a problem, only to receive an answer that feels disconnected or incomplete. Frustrating, right? The truth is, even the most sophisticated AI systems are only as good as the context they're given. Without the right information, even innovative models can falter, leaving you with results that miss the mark. In a world where AI is increasingly shaping industries and decision-making, mastering the art of providing precise and relevant context isn't just a nice-to-have—it's the key to unlocking next-level performance. Whether you're fine-tuning a language model or streamlining workflows, understanding how to manage context effectively can make the difference between mediocrity and excellence. All About AI reveal five powerful strategies to help you gather, organize, and optimize context for AI-driven tasks. From quick fixes like copy-pasting to advanced techniques like vector-based retrieval, these methods are designed to fit projects of all scales and complexities. You'll discover how to streamline your workflows, improve accuracy, and even future-proof your processes with tools like custom MCP servers and semantic search systems. But this isn't just about tools—it's about transforming the way you think about context itself. Ready to rethink how you approach AI performance? Let's explore what's possible when you master the foundation of all great AI: context. AI Context Management Tips 1. Copy-Paste for Quick Context Gathering One of the simplest and most accessible methods for gathering context is directly copy-pasting relevant information into your workflow. This approach is particularly effective for one-off tasks where immediate access to specific data is required. For instance, referencing a snippet of documentation or extracting a small dataset can be accomplished quickly and efficiently through copy-pasting. However, this method has its limitations. It is not scalable for complex or long-term projects, as it lacks structure and reusability. Over-reliance on copy-pasting can lead to disorganization and inefficiencies, especially in workflows that demand consistent access to large volumes of information. While it serves as a quick fix for basic tasks, it is not a sustainable solution for more advanced projects. 2. Organize with Local Context Storage For more structured workflows, local context storage provides a significant improvement. By organizing relevant documentation, datasets, and notes into local files or folders, you create a reusable repository of information that can be accessed whenever needed. This method is particularly beneficial for recurring tasks or projects that require frequent reference to the same materials. For example, if you are working on a project involving 3JS, you can store key documentation, tutorials, and examples in a dedicated folder. This eliminates the need to repeatedly search for the same resources, saving time and making sure consistency. By maintaining an organized local storage system, you can streamline your workflow and improve overall efficiency. Mastering AI Context For Improved AI Performance Watch this video on YouTube. Gain further expertise in AI context management by checking out these recommendations. 3. Use Web Search Integration Web search integration offers the ability to gather real-time context from online sources directly within your workflow. Modern browsers and integrated development environments (IDEs) often include built-in search functionalities, allowing you to retrieve information without interrupting your focus. This method is particularly useful for tasks that require up-to-date or dynamic information. However, the effectiveness of web search integration depends on the relevance and reliability of the sources. For example, searching for AI-related documentation may yield outdated or irrelevant results if the search queries are not well-defined. To maximize the benefits of this method, refine your search terms and critically evaluate the credibility of the sources you rely on. This ensures that the retrieved context is both accurate and applicable to your project. 4. Use Custom MCP Servers for Precision For projects that demand a higher level of control and precision, setting up custom Managed Context Processing (MCP) servers can be a fantastic option. Tools like Brave or Fetch allow you to tailor search queries and retrieve information that aligns closely with your specific project requirements. This method is particularly valuable for complex workflows where accuracy is paramount. For instance, if you are developing an AI model that relies on specific data from 3JS documentation, a custom MCP server can filter out irrelevant results and deliver only the most pertinent information. By customizing the retrieval process, you can ensure that the context you gather is highly relevant and precise, ultimately enhancing the quality of your work. 5. Adopt Advanced Vector-Based Context Retrieval The most advanced method for managing context involves the use of vector databases. These databases store information in a format optimized for semantic searches, allowing you to retrieve highly specific context based on the relationships between data points. This approach is particularly effective for large-scale or recurring tasks, as it reduces search time and improves precision. For example, if you are working on a 3JS project, you can populate a vector database with its documentation. By querying the database using natural language prompts, you can quickly and accurately retrieve the most relevant sections. This method not only enhances efficiency but also ensures that the retrieved context is directly aligned with your project's needs. Vector-based retrieval is an invaluable tool for managing complex workflows and achieving superior results. Effective Context Management for AI Success Mastering context management is essential for optimizing AI workflows and achieving exceptional results with language models. By combining basic techniques like copy-pasting with more advanced strategies such as vector-based context retrieval, you can tailor your approach to suit the complexity of your projects. Whether you are handling straightforward tasks or tackling intricate AI models, these methods provide the tools you need to enhance both performance and efficiency. By implementing these strategies, you can ensure that your AI projects are not only effective but also scalable and sustainable in the long term. Media Credit: All About AI Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Fast Company
16-05-2025
- Business
- Fast Company
Trust as a catalyst for rapid problem solving
Every delay in decision-making, execution, or problem-solving can have significant consequences. Success often depends on how quickly and effectively challenges are addressed. But there's one factor that consistently determines how efficiently problems get solved: trust. Over the years, I've seen that problems get resolved faster and solved better when trust is compelling. Clients who trust us to act in their best interests share their challenges openly, which allows us to bypass unnecessary roadblocks. Internally, teams with trust collaborate seamlessly, reducing inefficiencies and accelerating solutions. Trust is more than a feel-good concept; it's a measurable business advantage. According to a Deloitte study, 85% of B2B buyers say honesty and trustworthiness are essential to building long-term business relationships. In an industry where competition is fierce, being seen as a trusted partner isn't just beneficial, it's critical. I've learned firsthand that trust eliminates hesitation. When clients believe in our expertise and integrity, they don't hold back information or second-guess recommendations. Instead, they engage in open, productive discussions that fast-track problem-solving. For example, when faced with a technical challenge, the difference between solving it in days versus weeks often comes down to transparency. If a client hesitates to share all the details due to concerns about confidentiality or misalignment, the process slows down. But when trust is established early, we can work from a place of complete visibility, delivering precise, tailored solutions. This applies internally as well. Teams that trust each other don't waste time navigating office politics or double-checking every move. They focus on solutions, not on protecting themselves from blame. Trust fosters a culture where people feel empowered to contribute ideas, delegate effectively, and take ownership of their work. Beyond solving immediate problems, trust also drives long-term value. Clients who trust us involve us earlier in their decision-making process, allowing us to create proactive strategies rather than reactive fixes. This leads to better outcomes, stronger partnerships, and sustained success. Acting quickly is a competitive advantage in industries where time is a valuable resource. A trust-based relationship means clients don't wait until a problem escalates before bringing us in. Instead, they engage us early, eliminating bottlenecks and accelerating resolutions. Trust also fuels productivity. I've seen how teams perform when they operate in a high-trust environment versus a low-trust one. In low-trust workplaces, leaders micromanage, employees hesitate to take initiative, and innovation stalls. In contrast, high-trust environments encourage autonomy, accountability, and creativity. MANAGING EXPECTATIONS AND BUILDING LONG-TERM PARTNERSHIPS Trust is also at the heart of managing expectations effectively. One of the biggest challenges in B2B relationships is misalignment, when clients expect one outcome while the provider delivers another. A trust-based partnership eliminates these gaps by ensuring honest conversations about timelines, deliverables, and potential roadblocks from the start. Transparency in setting expectations prevents misunderstandings, allowing for proactive adjustments instead of reactive damage control. Clients appreciate honesty, especially when it comes to potential risks or limitations. In my experience, delivering bad news transparently always builds more trust than overpromising and underdelivering. Trust isn't just about solving today's problems, it's about building a foundation for future opportunities. When clients see that we act in their best interest, they return. When employees feel valued and empowered, they stay. And when partners know they can count on us, they invest in deeper collaboration. The ability to solve problems quickly and efficiently is a defining factor in high-stakes industries. But the key to unlocking that efficiency isn't just technical expertise or strategic planning, it's trust. As industries evolve, those prioritizing trust will meet and exceed expectations, creating value beyond short-term gains. Trust isn't just an advantage in an interconnected, competitive world, it's the driving force behind sustained growth, loyalty, and long-term success.
