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Our selection of the top business news sources on the web. |
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Quote: Lisa Su - CEO of AMD"Technology itself does not decide what the future looks like. People do." - Lisa Su - CEO of AMD Technological change never arrives as a purely technical event. A chip, model, platform or network creates possibilities, but its consequences depend on who controls access, which incentives shape deployment, what institutions permit and how workers, customers and citizens adapt. That distinction matters most when computing becomes general-purpose. Artificial intelligence can alter research, manufacturing, healthcare, education and public administration, yet none of those outcomes follows automatically from higher performance. Choices about design, ownership, distribution and accountability determine whether capability becomes broad productivity or concentrated power. Lisa Su's position carries particular weight because her career sits at the intersection of engineering and industrial strategy. As chair and chief executive officer of AMD, she has led the company's transformation into a high-performance and adaptive computing business, with a growing emphasis on AI infrastructure. Her public arguments place hardware within a much larger system: processors, memory, networking, software, data centres, developers and customers must work together before an impressive laboratory result can become a dependable service. AMD's recent strategy therefore presents AI not merely as a model competition, but as an ecosystem and supply-chain contest. The practical implication is that human agency operates at several levels. Engineers decide which problems to optimise and which constraints to accept. Executives decide where capital is allocated and whether platforms remain open or become tightly controlled. Governments decide how export controls, competition rules, safety standards and public procurement shape markets. Organisations decide whether automated outputs support professional judgement or quietly replace it. Individuals then encounter the consequences through employment, prices, services and access to information. The future is consequently produced by a chain of decisions rather than by an abstract force called technology. From invention to infrastructure AI makes this chain unusually visible because advances in software depend on physical resources. Training and running models require accelerators, processors, memory, networking, electricity, cooling and specialised engineering. Su told the US Senate Commerce Committee in 2025 that leadership in AI requires leadership in the infrastructure that powers it, while identifying open ecosystems, domestic semiconductor capacity, talent and a balance between security and adoption as strategic priorities. The argument shifts attention away from theatrical demonstrations and towards the less visible decisions that determine who can build and use advanced systems. AMD's own product narrative reflects that shift. The company describes an end-to-end portfolio spanning data-centre CPUs and GPUs, PC processors and embedded or adaptive systems, alongside open software and collaboration with partners. This approach is strategically rational in a market where performance is not the only bottleneck. Customers also need systems that can be procured, integrated, programmed, secured and operated at acceptable cost. An accelerator that cannot be supported by software, networking and skilled people may have impressive specifications but limited economic value. That does not mean technical capability is secondary. It sets the boundaries of what people can choose. More efficient computing can reduce the cost of inference and enable applications that were previously impractical. Better systems can expand scientific simulation, improve industrial control and assist workers with complex information. Yet increased capability also enlarges the range of possible misuse, from surveillance and fraud to automated discrimination and insecure decision-making. Human choice is therefore not a claim that technology is weak; it is a claim that power must be governed. The governance problem Responsible deployment requires more than asking whether a model is accurate in a benchmark. The relevant question is whether the complete socio-technical system performs acceptably in its real setting, including its data, users, incentives, interfaces and escalation procedures. The National Institute of Standards and Technology's AI Risk Management Framework organises this work around four functions: govern, map, measure and manage. Those functions imply continuing responsibility before deployment, during operation and after an incident, rather than a single compliance check at launch. NIST also distinguishes different human roles in AI systems, ranging from fully manual arrangements to systems that act autonomously, defer to experts or provide an additional opinion. This distinction is important because the phrase human oversight can conceal very different realities. A person who can technically override a system may lack the time, information or authority to do so. Conversely, requiring approval for every low-risk action can create fatigue and encourage automatic rubber-stamping. Effective agency depends on clearly assigned responsibility, meaningful intervention powers, monitoring and training suited to the consequences of error. There is a further objection to optimistic accounts of choice: many decisions are made under commercial pressure. A hospital may adopt an imperfect tool because budgets are constrained. A small business may accept a platform's terms because alternatives are unavailable. A worker may rely on automated scheduling because refusing it risks lost hours. In such conditions, consent is not always equivalent to control. Competition policy, labour protections, public standards and transparent procurement can widen the practical choices available to people, making agency an institutional property rather than merely an individual attitude. Why the debate matters for AMD For AMD, the principle has a direct strategic consequence. If AI adoption spreads across industries, the winning position will depend not only on supplying fast silicon but on enabling a diverse community of developers and organisations to build useful applications. Su has argued for open ecosystems because interoperability can reduce barriers to entry, encourage competition and broaden innovation. The case is commercially self-interested as well as civic: a wider developer base can increase demand for a platform, while customers are less likely to commit when they fear dependence on a single vendor. Open ecosystems, however, are not automatically open in practice. Access may still be limited by pricing, scarce capacity, proprietary interfaces, software maturity, energy requirements or shortages of technical staff. A nominally interoperable system can remain difficult to migrate if applications depend on vendor-specific optimisations. The meaningful test is whether customers can switch, audit, adapt and operate systems without prohibitive cost. That is where technical architecture meets market structure, and where claims about empowerment must be tested against evidence. The same tension appears in predictions about scale. AMD and other industry participants anticipate rapidly rising demand for compute as AI moves from experimentation into enterprise workflows and more autonomous software agents. Such growth could produce significant productivity gains, but forecasts are not outcomes. Productivity depends on redesigning work, distributing gains, protecting quality and ensuring that people possess the skills to use new tools. If investment flows mainly into infrastructure while training, safety and access lag behind, the resulting future may be technologically advanced but socially narrow. The most durable reading is therefore neither technological determinism nor human exceptionalism. Machines can reshape incentives, capabilities and institutions so profoundly that old forms of decision-making become impossible or uneconomic. People still decide which systems to build, who receives their benefits, whose risks are tolerated and what remedies exist when systems fail. That responsibility extends from semiconductor design and supply-chain policy to the final workplace interface. Treating those choices as unavoidable would itself be a choice, and usually one that favours whoever already controls the infrastructure.
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Term: Bollinger Bands - Technical analysis"Bollinger Bands are a technical analysis tool used to measure market volatility and identify potential overbought or oversold conditions by plotting three lines on a price chart: a 20-day simple moving average (middle band) and two standard deviation lines (upper and lower bands)." - Bollinger Bands - Technical analysis Trading decisions often fail when a relative price signal is mistaken for a forecast. A close near an outer boundary tells an analyst that price is unusually far from its recent average after adjusting for recent dispersion; it does not, by itself, establish that a reversal is due. The same observation can describe a powerful trend, a short-lived shock, or a market whose statistical behaviour has changed. This distinction gives the indicator practical value while placing firm limits on what it can say. How the calculation works begins with a moving average over a selected look-back period. For observations indexed by , a simple moving average with period is , where is the chosen price, usually the closing price. The corresponding standard deviation measures how widely those prices have dispersed around the average. If the multiplier is and the deviation is , the upper and lower boundaries are and . The familiar default uses 20 periods and a multiplier of 2, although these are conventions rather than universal rules. The calculation combines direction and dispersion without confusing them. The central average slopes upwards when the recent price path has a positive bias and downwards when it has a negative bias. The distance between the boundaries expands when recent prices become more variable and contracts when they become quieter. A useful normalised measure is band width, expressed as . This makes the indicator more comparable across instruments with different price levels, though it remains sensitive to the look-back period, the price field, and the chosen volatility estimator. Relative high and relative low are the proper starting points for interpretation. A price above the upper boundary is high relative to its recent distribution, while a price below the lower boundary is low relative to that distribution. It is not automatically overbought or oversold in the stronger sense of being destined to reverse. In a persistent advance, prices can remain close to, or repeatedly exceed, the upper boundary as volatility and trend reinforce one another. In a persistent decline, repeated contact with the lower boundary can reflect continued selling pressure rather than a bargain. That is why experienced users distinguish between mean-reversion and trend-following readings. A mean-reversion approach looks for evidence that an excursion is losing force, such as a failed continuation, a return inside the boundary, or confirmation from another measure of momentum. A trend-oriented approach treats sustained movement along an outer boundary as evidence of strength or weakness and uses the central average, recent swing structure, or a separate risk rule to define exposure. The same band contact can therefore produce opposite decisions under different market regimes. The indicator supplies context; the trading rule supplies the interpretation. Volatility regimes and the squeeze A pronounced narrowing of the boundaries is commonly called a squeeze. It signals that recent dispersion has fallen, not that a particular direction is guaranteed. Quiet conditions can precede a large move because compressed prices leave room for volatility to expand, but the eventual break may be upward, downward, or false. A disciplined process therefore waits for directional evidence, evaluates the size and persistence of the move, and considers whether volume, market breadth, macroeconomic news, or correlated assets support the signal. Narrow bands can also persist during uneventful markets, so timing a breakout solely from width is hazardous. The opposite condition, an expansion, indicates that recent movement has become more variable. It can occur during a genuine trend, after an information shock, or during disorderly price discovery. Expansion is not synonymous with opportunity: wider bands imply larger potential movements and therefore greater position risk. Traders who use a fixed number of shares or contracts may unintentionally increase portfolio risk when volatility rises. Position sizing, stop placement, and exposure limits should respond to the changing range rather than treating the chart distance as a guaranteed forecast. Parameter choice creates a trade-off. A shorter period reacts quickly but produces more frequent changes in the average and more unstable boundaries. A longer period smooths noise but may respond slowly when the market regime changes. A larger multiplier creates wider boundaries and fewer crossings; a smaller multiplier creates narrower boundaries and more signals. The default settings are useful for communication and comparison, but they should not be presented as optimal for every asset, timeframe, or objective. Parameters selected after inspecting the same data used to evaluate performance can create overfitting, especially when many combinations are tested. Statistical intuition also needs care. If observations were independent and normally distributed around a stable mean, a band placed two standard deviations from that mean would have a familiar approximate coverage interpretation. Financial prices rarely satisfy those assumptions: returns can be skewed, heavy-tailed, autocorrelated, and subject to volatility clustering. Moreover, the bands are calculated from a rolling sample and are applied to a price series whose level is often non-stationary. The result is a descriptive envelope, not a reliable probability forecast. A price outside a boundary may be unusual within the recent sample without being extreme in the broader market process. Evidence, debate, and practical use Research on technical trading strategies has produced mixed conclusions, and the apparent usefulness of a rule can vary by market, sample period, transaction costs, execution assumptions, and data treatment. One study reported that the predictive power associated with band strategies diminished after their publication and largely disappeared in later international evidence, a result consistent with competition reducing a once-visible anomaly. Other analyses find that outcomes depend heavily on the asset and regime, with false signals particularly common when a method is applied without a trend or volatility filter. These findings do not make the indicator useless; they show that a chart pattern is not the same as a durable economic edge. A robust workflow begins by defining the signal before looking at results. Specify the price field, period, multiplier, entry condition, exit condition, transaction costs, financing, slippage, and treatment of gaps. Test the rule on separate development and evaluation samples, then examine performance across assets and regimes rather than relying on one successful chart. Avoid treating a boundary touch as an executable price when the signal is only known at the close. For live use, record whether the trade followed a breakout, a confirmed rejection, or a broader portfolio rule, because otherwise a discretionary process can appear more systematic than it really is. Used carefully, Bollinger Bands remain a compact way to organise three questions: where price sits relative to its recent average, how variable recent prices have been, and whether a change in dispersion is altering the trading environment. They become more informative when paired with an explicit trend measure, momentum confirmation, liquidity checks, and risk controls. They become misleading when upper and lower boundaries are treated as automatic sell and buy levels. Their lasting relevance lies less in predicting the next price than in forcing analysts to distinguish relative extremity, volatility, and direction before committing capital.
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Global Advisors News Brief - September 28 2026Read the full brief at the link Headlines for the last 24hrs
Time window: 2026-09-27T05:00:33.073Z to 2026-09-28T05:00:33.073Z |
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Quote: Padma Lakshmi - TV Personality and Author"Time is the best luxury." - Padma Lakshmi - TV Personality and Author Luxury is often measured through rarity, price, and visible distinction, yet the scarce resource that governs all three is discretionary time. Money can purchase comfort, access, and assistance, but it cannot produce additional hours in a day. The practical value of time therefore depends less on its quantity than on control over its use: the ability to pause, choose, concentrate, recover, and give sustained attention to people or pursuits that matter. That distinction separates free time from merely unoccupied time. A person may technically have hours outside paid employment while remaining constrained by commuting, domestic labour, care responsibilities, administrative demands, illness, or the mental burden of constant availability. The World Bank defines time poverty through the combined pressure of insufficient rest and limited economic choice, including both paid work and unpaid household work . Time becomes luxurious when it is not simply present, but genuinely discretionary. The idea carries particular weight in an era when convenience technologies promise efficiency while expanding expectations. Faster communication, on-demand services, and flexible working can remove friction, but they can also extend work into evenings and weekends. The result is a paradox: more tools for saving time can create more obligations to answer, monitor, update, and optimise. The scarce commodity is not activity itself, but uninterrupted attention that has not already been claimed by someone else. From status object to lived capacity Traditional luxury markets sell objects and experiences that signal access to scarce resources. A private journey, a carefully prepared meal, or an exclusive retreat has value partly because it compresses effort for the buyer and transfers time-consuming work to specialists. The appeal is not only craftsmanship or prestige. It is the removal of logistical burdens, allowing the customer to inhabit a slower and more controlled rhythm. This helps explain why hospitality, wellness, travel, domestic services, and premium food have increasingly converged around the language of time. A high-end service often promises fewer decisions, shorter queues, reliable quality, or a protected setting in which attention can settle. Its value lies in what it prevents: rushing, coordination, uncertainty, and the fragmentation produced by too many competing demands. In that sense, luxury is frequently a purchase of reduced friction. Padma Lakshmi's public career makes the relationship between time, labour, and consumption especially relevant. Her work spans television production, food writing, travel, advocacy, and authorship, rather than a single neatly bounded profession . Such a career illustrates that visible success can conceal dense schedules and substantial invisible preparation. Public authority may look effortless on screen, while depending on research, travel, editing, negotiation, caregiving, and repeated performance away from the audience. The unequal distribution of choice Time is not distributed neutrally. The World Bank reports that women are significantly more likely than men to experience time poverty, because paid employment is combined with domestic and caring responsibilities . More recent work also distinguishes the amount of time available from the ability to decide when activities occur. World Bank research finds that women are 20 percentage points more likely than men to report inflexibility in adjusting the timing of activities across areas including unpaid work, transport, leisure, and personal care . This distinction matters because a free hour at an inconvenient moment is not equivalent to a free hour that can be used meaningfully. Someone waiting for a dependent person, working irregular shifts, or responding to unpredictable demands may have nominal gaps but little reliable autonomy. Time poverty is therefore not only a shortage of hours. It is a shortage of control, predictability, and recovery. Academic research has also challenged the assumption that income alone captures material wellbeing. The study of time poverty examines whether households possess enough time for rest, leisure, and necessary domestic production alongside sufficient income . This creates a more demanding account of inequality: two people can earn similar amounts while facing radically different burdens of cooking, cleaning, care, travel, and emotional administration. The person with more control over these demands possesses an advantage that may not appear in conventional financial measures. The strategic tension between efficiency and presence Businesses understand the commercial value of time, but they often approach it through speed. Faster delivery, instant payment, rapid customer support, and shorter production cycles can be valuable, especially when they reduce anxiety or make essential services accessible. Yet speed is not identical to luxury. Premium experiences often depend on deliberate pacing, human attention, and enough duration for quality to become perceptible. A meal rushed through a prestigious setting does not deliver the same value as one enjoyed without interruption. The tension is particularly visible in professional life. Organisations seek productivity by compressing tasks, measuring response times, and multiplying output. Individuals, meanwhile, need slack: unused capacity that allows reflection, error correction, learning, and rest. Eliminate all slack and efficiency becomes brittle. A schedule with no reserve may maximise immediate utilisation while reducing judgement, creativity, and resilience. Time used for recovery is often treated as unproductive, even though it protects the capacity to perform future work. Technology intensifies this tension by making attention easier to divide. Notifications, collaborative platforms, and algorithmic feeds convert small intervals into opportunities for engagement. The resulting fragmentation is difficult to measure because the person may remain active throughout the day, yet complete fewer tasks requiring deep concentration. The premium attached to uninterrupted time is consequently rising, whether it appears as a device-free room, a protected calendar block, or a social norm that permits delayed replies. Objections and limits There are legitimate objections to treating time as luxury. Time cannot be detached from income, health, legal status, geography, or public infrastructure. A person may have nominal leisure but lack safe housing, accessible transport, medical care, or the money required to use free time well. Romanticising slowness can therefore obscure the material conditions that make rest possible. For people living under financial pressure, the problem is not simply that time has been poorly prioritised; reducing work may threaten household survival. A second objection concerns the language of personal choice. Advice about boundaries, mindfulness, or digital restraint can imply that individuals are solely responsible for pressures created by employers, unequal care systems, and economic insecurity. Personal scheduling has value, but it cannot substitute for fair working conditions, affordable childcare, sick leave, shorter commutes, or recognition of unpaid labour. The political meaning of time depends on who has the power to refuse demands without severe consequences. Even so, the concept remains useful because it exposes a form of wealth that conventional luxury narratives often hide. The most meaningful advantage may be the ability to decide what deserves attention and to remain with it long enough for value to emerge. Time spent cooking with family, recovering from illness, reading carefully, or building a difficult skill may have no obvious market price, yet these activities can constitute a richer form of prosperity than conspicuous consumption. That is why time has become an increasingly precise test of privilege and wellbeing. Its value rises when it is protected from compulsion, fragmentation, and fear. The broader implication is not that every slow experience is superior, but that autonomy over duration is a serious social and economic resource. Luxury, understood in this deeper sense, begins where a person can use time deliberately rather than merely survive its demands.
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Term: Accounting Rate of Return (ARR) - Financial ratios"The Accounting Rate of Return (ARR) is a financial metric that measures the expected profitability of a capital investment by dividing its average annual accounting net profit by the initial or average investment cost, expressed as a percentage." - Accounting Rate of Return (ARR) - Financial ratios Capital decisions often begin with a practical question: how much accounting profit will a proposed asset or project generate relative to the capital committed to it? The Accounting Rate of Return, or ARR, answers that question through a percentage based on expected accounting income. It is therefore a profitability screening measure rather than a complete valuation technique. Its usefulness lies in speed, familiarity and a direct connection with reported performance; its weakness is that accounting profit is not the same as cash generated or economic value. In substance, ARR compares average annual accounting profit attributable to an investment with either the initial investment or the average investment tied up over the asset's useful life. The commonly used expressions are and, where average capital is preferred, . The first denominator is straightforward and convenient for internal screening. The second can better reflect the declining book value of a depreciating asset, but it also introduces a further accounting convention into the comparison. Average annual accounting profit normally means the total expected accounting profit over the investment period divided by the number of years. It is not simply annual revenue and it is not ordinarily the same as annual cash inflow. Incremental revenues are reduced by incremental operating expenses, including depreciation, and the resulting profit may be measured before or after tax according to the organisation's policy. If an existing asset is replaced, the relevant investment may be adjusted for disposal proceeds, while salvage value and working capital may be included or excluded depending on the stated method. These choices must be made consistently because a change in depreciation, useful life or tax treatment can alter ARR without changing the project's underlying cash economics. A simple illustration shows the mechanics. Suppose a project requires an initial investment of 240 000 and is forecast to produce total accounting profit of 90 000 over five years. Average annual profit is , so the initial-investment version of ARR is . If the asset has a zero terminal book value and management uses average investment, the denominator may be , producing . Both results can be correctly calculated under their respective conventions, yet they imply materially different judgements. That is why an ARR report should always identify its denominator and profit basis. How organisations use the ratio Management usually compares ARR with a hurdle rate, target return or divisional benchmark. A project at or above the required percentage may pass an initial screen, while a project below it may be rejected or sent for further review. The measure is particularly convenient when managers already have forecast income statements and want a rapid indication of whether an asset is expected to improve accounting performance. It can also support post-investment review by comparing forecast ARR with the return subsequently recorded in the accounts. In that role, it is a performance measure as much as an investment appraisal measure. ARR has several legitimate strengths. It is easy to explain to non-specialists, uses familiar profit concepts and can incorporate operating costs that a purely cash-focused shortcut might overlook. Unlike the payback period, it does not stop when the original outlay has been recovered; it considers accounting profit across the stated investment period. This can make it useful for a first-pass comparison, especially where projects are similar in scale, duration, risk and accounting treatment. Open educational guidance identifies simplicity and the focus on incremental income as important advantages, while also placing ARR among methods that do not adjust for the time value of money. The principal theoretical problem is that ARR treats profit earned at different dates as if each pound had the same value. A profit recorded in the first year receives the same weight as an equal profit recorded in the fifth year, even though the later amount is less valuable when discounted at a positive required return. ARR therefore cannot determine whether a project creates value in present-value terms. Net present value instead discounts each relevant cash flow, using a relationship such as , where is the cash flow in period and is the discount rate. A positive NPV provides a more economically grounded acceptance rule than a percentage threshold based only on average profit. The distinction between profit and cash creates a second limitation. Depreciation reduces accounting profit but is not itself a cash payment in the period recorded. Conversely, working-capital movements, tax payments, opportunity costs and disposal proceeds may affect economic cash flow without appearing in ARR in the same way. Timing also matters for liquidity: a project can report an acceptable average profit while producing weak early cash flows that create financing pressure. Because ARR averages results, it may conceal a sharp decline, a back-loaded benefit or a loss in an individual year. ACCA specifically notes that ARR considers profits rather than cash flows and does not consider the timing of cash flows, which can make it unsuitable as a stand-alone decision rule. Debates over comparability and accounting policy There is no single universally applied ARR convention. Some organisations divide by initial investment, others use average book value, and some adjust the denominator for residual value or working capital. Profit may be defined before or after tax, and depreciation may follow straight-line or another method. These choices can change project rankings even when expected cash flows remain constant. The ratio is therefore most reliable when used within one organisation under a clearly documented policy, rather than as an unqualified comparison between companies or sectors. A high ARR may reflect a low accounting denominator, favourable depreciation assumptions or a different profit definition rather than superior economic performance. Major schools of thought differ mainly over the role assigned to accounting information. The traditional managerial-accounting view treats ARR as a simple supplementary measure: useful for communicating expected earnings and checking alignment with internal targets, but inferior to discounted cash-flow methods for value creation. A value-based finance view gives priority to NPV because investment should be accepted when the present value of expected cash inflows exceeds the present value of required outflows. IRR can add a percentage-based perspective, but it too depends on cash flows and can produce ambiguous results for unconventional projects. The sensible synthesis is not to discard ARR, but to prevent a familiar ratio from being mistaken for a complete economic analysis. ARR still matters because capital decisions are made in organisations that care about both cash value and accounting outcomes. Reported profit can influence performance assessments, debt arrangements, management incentives and stakeholder perceptions, so an investment that creates cash value but depresses early accounting earnings may face internal resistance. ARR can expose that accounting impact and provide a common language between operational managers and finance teams. It should, however, sit alongside NPV, cash-flow forecasts, sensitivity analysis, risk assessment and strategic considerations such as regulatory compliance, resilience, safety or environmental effects. Used as a transparent screening ratio with its assumptions stated, ARR is informative; used alone to rank projects, it can produce decisions that look profitable in the accounts but destroy value in practice.
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Global Advisors News Brief - September 27 2026Read the full brief at the link Headlines for the last 24hrs
Time window: 2026-09-26T05:00:33.072Z to 2026-09-27T05:00:33.072Z |
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Quote: Confucius - Chinese philosopher"Real knowledge is to know the extent of one's ignorance." - Confucius - Chinese philosopher Intellectual humility begins with a boundary: the ability to distinguish an examined judgement from an assumption, a verified fact from a plausible story, and a gap in understanding from an answer that merely feels complete. That boundary is not a retreat from learning. It is the condition that makes learning possible, because a person who cannot identify uncertainty has no reliable reason to investigate, revise or listen. The teaching associated with Confucius appears in Book 2, section 17 of the Analects, where the disciple Zilu is instructed to recognise what he knows as known and what he does not know as unknown. The popular English wording supplied here is therefore best treated as a modern paraphrase, rather than a literal translation of the classical text. That distinction matters because the original emphasis is not simply on possessing less confidence. It defines knowledge as accurate self-location: knowing the status of one's own judgement. This is a more demanding standard than modesty as a social performance. Someone may speak softly, disclaim expertise and still remain careless about evidence. Conversely, a person may reach a firm conclusion without arrogance when the available evidence is strong and the reasoning is open to inspection. The practical test is calibration, meaning whether confidence corresponds to the quality and limits of the grounds supporting it. Intellectual humility is consequently an active discipline of proportion, not a demand that every claim be treated as equally uncertain. Knowledge as disciplined self-assessment The Confucian setting gives this discipline a moral and social dimension. Knowledge in the Analects is not presented chiefly as an inventory of abstract facts. It is closely connected with appropriate conduct, cultivated judgement and the ability to act well within relationships and institutions. Recognising ignorance prevents a person from converting partial understanding into authority. In public life, that restraint can protect others from the consequences of decisions made with confidence unsupported by competence. The image of the sage as an all-knowing figure is also complicated by another passage in the Analects, where Confucius denies possessing unlimited wisdom and describes working through a question by examining its sides until clarity develops. The point is not that uncertainty is itself virtuous in every circumstance. Rather, a responsible learner responds to uncertainty with inquiry. Admission becomes useful only when it changes conduct: prompting a request for evidence, a conversation with someone more experienced, a test of competing explanations or a revision of the original view. Modern research on Confucian and Daoist approaches to ignorance similarly describes the boundary between knowing and not knowing as flexible and context-dependent. It links ignorance not merely with deficiency but with humility, education and the recognition that personal experience is limited. This interpretation helps explain why the teaching remains relevant beyond philosophical study. A person can be highly informed in one domain and inexperienced in another, while expertise itself can become a source of error when it is transferred without care. Why the distinction matters in an information economy Contemporary institutions often reward fluency before they reward accuracy. Fast communication, professional hierarchy and digital platforms create incentives to answer immediately, even when the question is ambiguous or the evidence incomplete. A confident statement can travel further than a qualified one, and repetition can make a claim appear established without improving its truth. Under these conditions, the failure to mark ignorance becomes an operational risk rather than a private character flaw. The danger is especially visible in complex systems. Public health, financial markets, climate policy and artificial intelligence involve interacting variables, delayed effects and incomplete observations. Decision-makers rarely possess a complete model of the situation. They must therefore separate known measurements, reasonable assumptions and unresolved uncertainties. A forecast may be useful without being certain, but its usefulness depends on communicating the conditions under which it could fail. That requirement also applies to artificial intelligence and automated decision-making. Systems can produce coherent language while lacking dependable awareness of whether a statement is supported. Users who mistake fluency for knowledge may accept fabricated citations, incorrect summaries or unsupported causal claims. The underlying lesson is not that technology must remain silent. It is that every output needs an account of provenance, confidence and limitation, especially where errors affect rights, safety or significant financial decisions. Objections to humility There are legitimate objections to making ignorance sound too noble. Excessive hesitation can delay action, weaken accountability and allow people to avoid decisions by endlessly requesting more information. Institutions also need specialists who can state conclusions clearly. A surgeon, engineer or regulator cannot treat every established principle as an open question merely because absolute certainty is unavailable. The stronger interpretation avoids that problem by distinguishing uncertainty from indecision. Knowing the extent of ignorance includes knowing what is sufficiently established for action and what remains conditional. It supports decisive behaviour when evidence warrants it, while requiring a mechanism for correction when circumstances change. In this sense, humility improves authority by making its basis visible. It tells others not only what a person believes, but how far that belief can reasonably travel. Another objection is that the phrase can be used to flatten expertise. If everyone is told that they know little, genuine differences in training and evidence may be obscured. A specialist's informed uncertainty is not equivalent to a stranger's uninformed suspicion. The answer is not to reject humility, but to combine it with standards: relevant experience, transparent methods, reproducible evidence and willingness to engage with criticism. Humility should lower unjustified certainty, not erase justified distinctions. A practical method for recognising the boundary The principle can be translated into a simple practice. Before making a consequential claim, identify which parts are directly observed, which are inferred and which depend on assumptions. Ask what evidence would change the conclusion, who has relevant expertise and whether the language being used implies more precision than the evidence permits. This process turns self-awareness into a form of quality control. In conversation, the discipline is equally concrete. Replacing an unqualified assertion with a precise statement such as 'the evidence currently supports' or 'I do not have enough information to determine this' is not rhetorical weakness when the wording is accurate. It gives others a clearer basis for response. It also creates room for correction without forcing the speaker to defend an exaggerated position simply to preserve status. The enduring value of the Confucian teaching lies in this union of epistemic accuracy and ethical responsibility. Learning is not measured only by how many answers a person can recite. It is also measured by whether the person can identify the limits of those answers, respond intelligently to what lies beyond them and avoid making ignorance dangerous to others. In a culture crowded with information and rewarded certainty, that capacity remains a demanding form of wisdom.
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Term: Game theory - Applied mathematics"Game theory is a branch of applied mathematics that studies strategic interactions among rational decision-makers, where the outcome for each participant depends on the choices made by all. It structures these interactions as 'games' consisting of players, their available strategies, and the resulting payoffs." - Game theory - Applied mathematics Strategic interdependence changes the logic of decision-making. When one person's result depends on what others do, choosing well requires more than comparing private costs and benefits. It requires anticipating responses, recognising incentives, and assessing how an entire pattern of choices may produce an outcome that no participant intended. This is why game theory sits between mathematics, economics, politics, biology, psychology and computer science: it offers a disciplined way to analyse situations in which actions are mutually connected. A formal game normally identifies players, the actions or strategies available to them, the information they possess, the timing of decisions, and the payoffs associated with possible outcomes. A player may be an individual, a firm, a state, an algorithm, or a population of organisms. A strategy is not merely a single move. In an extensive game, it is a plan specifying what a player would do at every decision point that might be reached. Payoffs represent preferences over outcomes and need not be limited to money; they may include security, reputation, market share, votes, survival or fairness. The distinction between a decision problem and a game is practical. A firm with a guaranteed monopoly can often select output by comparing prices and costs without modelling a rival's reaction. In an oligopoly, by contrast, a price cut may provoke retaliation, a capacity expansion may deter entry, and a public commitment may alter competitors' expectations. The relevant question becomes not simply which action is best, but which action remains best after other players respond. Game theory therefore examines best responses and the feedback loops generated by interdependent choices. How the mathematics works A strategic-form game can be represented by a set of players, a strategy set for each player, and a payoff function for each player. For player , the payoff can be written as , where is that player's strategy and denotes the strategies chosen by everyone else. A Nash equilibrium is a profile satisfying for every player and every feasible alternative . In plain language, no player can gain by changing strategy alone while the others keep theirs fixed. This condition is important but limited. It defines stability against unilateral deviation, not social desirability, fairness or truth. Several equilibria may exist, and some may be inefficient for everyone. In the prisoners' dilemma, individually rational choices can generate mutual defection even though both players would prefer mutual cooperation. Mixed strategies extend the framework by allowing players to randomise, with probabilities chosen so that opponents are indifferent or best responding. Nash proved that every finite non-cooperative game has at least one equilibrium when mixed strategies are permitted, a result that established a broad existence guarantee rather than a universal prediction of behaviour. Major traditions and competing assumptions Non-cooperative game theory studies situations in which agreements are not automatically enforceable. Its central tools include Nash equilibrium, subgame-perfect equilibrium for sequential decisions, and refinements that eliminate implausible threats or implausible beliefs. Cooperative game theory instead focuses on coalitions, bargaining and how gains can be distributed among groups. In a cooperative model, the analytical problem may concern whether a coalition can form, whether an agreement is stable, and how its surplus should be allocated rather than which isolated action each player selects. Evolutionary game theory relaxes the idea that every player calculates perfectly. It studies how strategies spread when their relative success affects reproduction, imitation or persistence. A strategy can survive because it performs well against the population, even if no organism consciously understands the underlying incentives. Behavioural game theory goes further by testing formal predictions against laboratory and field evidence. People frequently use heuristics, respond to framing, value reciprocity, punish unfairness, and make systematic errors. These findings do not make mathematical models useless; they clarify which assumptions are descriptive approximations and which are normative benchmarks. Applications and practical meaning In economics, game theory helps explain competition, auctions, bargaining, contracting and market entry. Regulators use it to assess how firms may respond to rules, disclosure requirements or penalties, although the quality of any conclusion depends on the specified information and payoffs. In politics and international relations, deterrence, negotiation and arms control involve credibility: a threat matters only if carrying it out remains rational when the relevant moment arrives. In public policy, the framework exposes free-rider problems, in which each actor benefits from a shared resource while preferring others to bear the cost of maintaining it. Technology has expanded the range of applications. Network users affect congestion, platforms design rules for interacting sides of a market, and online systems must anticipate strategic manipulation. Mechanism design reverses the usual direction of analysis: rather than asking what players will do under fixed rules, it asks how rules can be constructed to produce desired outcomes despite self-interested behaviour. This approach informs auction design, matching markets and elements of digital governance. Computer science also studies algorithmic games in which computation, information asymmetry and automated agents alter the meaning of speed, commitment and strategic adaptation. Where the framework can fail The hardest modelling choice is often not solving the game but defining it. Payoffs may be uncertain, incomplete or socially contested. Players may misunderstand the situation, lack the capacity to calculate, or care about norms that are difficult to encode. A model that treats reputation as irrelevant can misread repeated interaction; a model that assumes perfect information can overlook bluffing and signalling; a model that assigns a single objective to a government or firm can conceal internal conflict. Predictions are therefore conditional: they describe what follows from a specified structure, not what must happen in reality. Equilibrium also says less than its technical authority can suggest. Existence does not guarantee uniqueness, stability over time or empirical accuracy. If several equilibria are possible, institutions, history, focal points and communication may determine which one emerges. If the game changes as players learn, then a one-shot solution may be inappropriate. Repeated games, reputation models and learning dynamics address some of these problems, but they introduce additional assumptions. The practical discipline is to compare alternative specifications, conduct sensitivity analysis, and distinguish an equilibrium prediction from evidence about actual conduct. Why it still matters Game theory remains valuable because it makes hidden dependence explicit. It forces analysts to ask who can act, what each participant wants, what information is available, which commitments are credible, and how incentives change after each response. Its greatest contribution is not the promise of perfect prediction. It is a common language for analysing conflict, cooperation and coordination across fields that otherwise describe similar problems in incompatible terms. Used carefully, it reveals why individually sensible choices can create collective problems and how altered rules, information or payoffs can change the set of available outcomes.
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Global Advisors News Brief - September 26 2026Read the full brief at the link Headlines for the last 24hrs
Time window: 2026-09-25T05:00:33.073Z to 2026-09-26T05:00:33.073Z |
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Quote: Leonard Lauder - Former CEO of Estée Lauder"Fantasizing, projecting yourself into a successful situation is the most powerful means there is of achieving personal goals." - Leonard Lauder - Former CEO of Estée Lauder Ambition becomes operational only when a desired future changes what a person notices, attempts, measures, and refuses to abandon. Mental projection can supply that change by making an abstract objective concrete enough to guide attention. A person who can picture a promotion, a completed manuscript, or a viable company is more likely to recognise relevant opportunities and organise effort around them. Yet the value of imagined success depends on whether it produces disciplined action rather than a pleasant substitute for action. Leonard Lauder's career gives the idea a demanding business context. He joined the family company in 1958 after naval service and helped move it beyond a single-brand operation. He served as president from 1972 to 1995 and chief executive from 1982 to 1999, later becoming chairman and then chairman emeritus. During those decades, Estée Lauder developed brands including Aramis, Clinique, Lab Series, Origins, M.A.C, Bobbi Brown, Aveda, Jo Malone London and La Mer, while expanding internationally. The relevant lesson is not that imagination guarantees scale, but that a large outcome begins as a representation of what could be built and is then tested through systems, people, products and capital. Lauder's record also complicates the idea of individual manifestation. He inherited a family enterprise with an established identity, relationships and operating knowledge, even though he substantially broadened its reach. The company states that it grew from eight products in one country into a global, multi-brand business during the family's stewardship. Such a trajectory reflects imagination, but also timing, consumer research, distribution, professional management, research and development, brand architecture and acquisition strategy. A vision can identify a direction; it cannot by itself manufacture demand, talent or resilience. From fantasy to directed attention Imagining a successful outcome can influence behaviour through attention and preparation. A vivid target supplies a mental reference point against which present choices are judged. It can make a difficult conversation worth initiating, a skill worth practising, or a short-term sacrifice easier to tolerate. The mechanism is strongest when the imagined future is specific enough to imply behaviours. 'Become successful' offers little instruction; 'lead a product launch that earns repeat customers' points towards research, testing, communication and follow-through. Psychological research draws an important distinction between merely imagining a positive future and mentally contrasting that future with the obstacles standing between the present and the goal. A review of mental contrasting describes growing evidence that the technique can improve motivation and performance in behaviour-change settings. The practical sequence is straightforward: define a desired outcome, imagine its benefits, identify the present obstacle, and choose a response. This converts aspiration into a problem-solving process rather than treating positive emotion as evidence that progress has occurred. The discipline behind the image Implementation intentions add another layer by linking a foreseeable situation to a planned response. The structure can be represented as . For example, a founder might decide that if customer interviews reveal the same defect three times, then the team will pause expansion and revise the product. Such planning reduces the need to improvise at precisely the moment when pressure, fatigue or optimism might distort judgement. Recent research qualifies any simple claim that visualisation alone produces achievement. A 2025 study found that implementation intentions encouraged spontaneous mental imagery and improved the target behaviour compared with a general goal intention, but participants who spontaneously visualised did not achieve more than those who did not. The finding supports a narrower interpretation: imagery may accompany effective planning, but it is not necessarily the active ingredient. What matters may be the link between a goal, an obstacle and a concrete response. This distinction matters because positive fantasy can sometimes reduce urgency. If a person repeatedly enjoys the emotional reward of an imagined success without confronting its costs, the mind may treat the desired state as psychologically complete. The result can be premature satisfaction, inflated confidence or avoidance of disconfirming evidence. Mental rehearsal is therefore more useful when it includes difficult scenes: rejection by a buyer, a failed experiment, a missed deadline, a rival's advantage or a shortage of cash. Imagining obstacles does not weaken ambition; it gives ambition a route through reality. Strategy, risk and organisational scale For leaders, projection is not only a private motivational practice. It is a method of allocating scarce resources. A future image influences which markets receive investment, which capabilities are developed and which opportunities are dismissed. Lauder's expansion of Estée Lauder involved building research capacity, introducing professional management and pursuing international growth, according to the company. Those choices show how a strategic picture becomes consequential only when translated into organisational infrastructure. The same process creates risk. A compelling vision can unify employees, but it can also create conformity if dissent is interpreted as disloyalty. Leaders who identify too strongly with a projected future may continue funding a weak strategy because abandoning it feels like abandoning themselves. Independent measures are therefore essential. Revenue quality, retention, product performance, cash generation and customer evidence can challenge a seductive narrative before it becomes expensive. A goal should motivate investigation, not replace it. Why the idea still matters The statement remains useful when read as a claim about direction rather than magic. People act towards futures they can represent, and representation influences attention, persistence and choice. But achievement is jointly produced by imagination and correction. The most effective mental picture includes the desired result, the next measurable action, the likely obstacle and the evidence that would require a change of plan. Lauder's example is instructive precisely because it joins aspiration to execution. His leadership was associated with brand launches, acquisitions, international expansion and a more formal operating platform, not with desire separated from work. The practical implication for an individual is modest but powerful: picture the outcome clearly, identify the constraint that could prevent it, specify the behaviour that will address that constraint, and review the evidence without protecting the fantasy. That sequence turns a future image into a working instrument.
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