This post continues my analysis of the digital spreadsheet as a meaning-making technology operating globally. It addresses two issues that have hindered my previous research progress – substitution and action. How do “signs” of things and people substitute for the real world? Also, how do spreadsheets lead to actions and decisions?

Consequently, I have reached further into semiotic and post-structural theories to introduce and examine two conjuncted terms, “writing-as-substitution,” and “computation-as-symbolic-action.” They will be used as frameworks to analyze the epistemological workings of spreadsheets, and even extrapolate to other spreadsheet-like technologies, such as financial terminals like the Bloomberg “Box.”[1]

The post develops a comparative semiotic framework for understanding how spreadsheet-based and algorithmic systems govern human and institutional behavior through substitution and computation-as-symbolic-action. Drawing from Charles Sanders Peirce’s triadic semiotics and Jacques Derrida’s notion of différance and trace, it argues that media systems such as spreadsheets, dashboards, and AI models are not neutral tools but semiotic infrastructures that produce and enforce meaning through symbolic substitution, deferral, and interpretive feedback, enabling a form of technocapitalist governance grounded in semiotic abstraction.[2]

By situating formulas, metrics, and predictions within the dynamics of Peircean interpretants and Derridean traces, this post offers a critical perspective on the growing power of algorithmic systems to structure visibility, automate judgment, and inscribe control in modern information and communications technologies.

Constructed Knowledge Through Substitutional Systems

Semiotic scholars point out that language and writing remove the visible from sight, replacing it with a conceptual or textual substitute (or trace). For Charles Sanders Peirce, every sign exists within a triadic relationship consisting of a Representamen (the sign itself), the Object (what it refers to), and an Interpretant (the understanding or effect produced in a mind or subsequent sign). The interpretant is not a static meaning but a mediating process. Meaning unfolds through a recursive chain of interpretations rather than being fixed in any single instance.

In the context of spreadsheets, each numerical or textual cell can be understood as a representamen that stands for an external reality, such as a mortgage, a barrel of oil, or a share price. The interpretant occurs when an analyst, trader, or algorithm reads this value, interprets its significance, and transforms it into a new action or representation (e.g., an updated forecast, a buy/sell signal, or a risk model).

Writing recovers the world as Derrida’s trace. Where Peirce’s interpretant emphasizes continuity, Derrida’s trace emphasizes absence. For Derrida, every sign carries within it the trace of other signs that it both recalls and excludes. Meaning is not generated by correspondence but by difference and deferral (différance). The trace marks what is not present. It marks the remainders of prior substitutions and the anticipation of future ones.

Spreadsheets work through entering alphanumerical symbols that make something visible on the spreadsheet that is materially, but not entirely, absent.[3] This inherently means that words and numbers substitute for presence — they represent something absent, creating a system where presence is always deferred through representation.[4] Language becomes a ghosting of presence. For Derrida, the thing represented is hidden, but its representation remains.

In semiotic parlance, the spreadsheet is a gridwork of signs where each cell is an empty container waiting to receive inputs that substitute for real-world quantities, relationships, or categories. As Ferdinand de Saussure argued, language operates not through a natural bond between a word (signifier) and its object (signified), but through arbitrary, differential relationships within a structured system.[5] Spreadsheet cells gain meaning not in isolation, but by their position within a grid (row/column), the labels assigned in headers, and their formulaic relations.

In Derrida’s spreadsheet, each number or letter also bears the logic of the trace. A cell labeled “Revenue” or “SOFR” seems to present a concrete fact. Still, in Derridean terms, it is a deferred presence, a placeholder for a complex, deferred chain of other inscriptions. These could include contracts, transactions, data feeds, and institutional protocols. The number “5.25%” in a Bloomberg cell, for instance, is not an empirical reality but a symbolic residue of an entire infrastructural network of rate submissions, calculations, and policy expectations. Where Peirce’s interpretant focuses on how signs generate meaning through interpretation, Derrida’s trace reveals how meaning is haunted by what cannot be fully represented. The messy, material world that must be abstracted away to make computation possible.

In the spreadsheet, writing becomes substitution. Jacques Derrida would identify this substitution as the “trace.” The number “125” in a cell is not an object itself (e.g., 125 cows or units of revenue) but a symbolic stand-in: a visible mark that references an absent event, transaction, or condition. As Derrida noted in Of Grammatology, writing is “a substitute which inscribes absence.”[6] The spreadsheet thus removes the visible (the real-world referent) from sight and replaces it with a structured trace. The spreadsheet governs through symbols that don’t mean directly, but produce meaning through absence. Words, in this framework, do not point directly to what they mean, but to a network of traces — each word carries the echo of another. This absence is not a deficiency but a condition that allows for interpretation and meaning to emerge. Writing is not secondary to speech; rather, it demonstrates the displacement of the referent.

In spreadsheets, this process of knowledge construction unfolds through several processes.

– List labeling (column headers like “Net Profit,” “Customer Churn,” or “Risk Tier”)

– Formulaic logic (statistical functions like standard deviation (stdev) =STDEV, financial models using =NPV, etc.)

– Conditional computation (e.g., =IF, =VLOOKUP, =INDEX/MATCH)

These processes substitute conceptual abstractions for real-world activities such as sales, behaviors, failures, and strategies. In an organizational or personal productivity context, these elements are translated into a symbolic, calculable form. In this system, meaning is never fixed, and what appears “present” is only ever a trace. The written word functions not by presenting, but by replacing presence with representation—leaving behind traces of what is no longer visible.

How Functions and Formulas Operate Semiologically

Formulas and functions are types of writing. A formula such as =SUM(B2:B5) is a syntactic construction written in a symbolic language. They substitute for material reality and use formulas to stand in for an action. A substitution of the act of summing for an observable process. It replaces manual calculation with a rule-based trace. The value the formula returns is not immediately visible in the formula itself. Rather, it is calculated in absence, echoing Derrida’s idea that signification is always deferred and built upon an invisible referent.

Spreadsheets act relationally. Much like Saussure’s linguistic signs, formulas derive their value by referring to other cells (e.g., B2:B5), which in turn may themselves contain signs or substitutions. Derrida’s différance plays out materially in spreadsheet logic as each function defers meaning by referencing other parts of the sheet. The result is a trace of invisible logic, embedded in visual structure.

The formula result references absent and mutable data. It is a deferred, procedural statement whose outcome is dependent on other absent or invisible data. Spreadsheet grid logic organizes and constrains sign use. A formula like =IF(D3>100,”High”,”Low”) is not a result, but a script for producing conditional presence. Meaning is always deferred. Derrida invented the term différance to refer to the production of meaning through dependencies, ranges, and conditionalities.

The digital spreadsheet is a semiotic and epistemological technology, combining writing-as-substitution with computation-as-action within a structured symbolic field. It does not merely reflect the world; it replaces, structures, and governs it. Through formulas and functions, it mediates between symbol and action, data and decision, absence and presence. It constructs reality in grids, calculates from traces, and produces futures through procedural substitutions. In this way, the spreadsheet is not just where information is stored; it is where information becomes action, knowledge becomes structure, and representation becomes rule.

Jacques Derrida reoriented Saussure by emphasizing that writing is not a mirror of speech or presence. Writing is a trace, a mark of absence, and an act of deferred meaning. In spreadsheet logic, a formula like =VLOOKUP(D5,Table,2,FALSE) is a grammatological trace. It invokes a value not immediately present, referencing an array of values elsewhere. The value that appears in the cell is not the thing itself, but the outcome of a substitution chain.

Thus, the spreadsheet calculates from traces, not from essences. As Derrida would argue, every function is a gesture of absence, producing presence through reference to the elsewhere. Spreadsheet returns and an act of symbolic dislocation are made manifest through computation. The spreadsheet is not a passive record but a machine of deferrals, where value is always calculated in relation, never in isolation, and fully present in its textual inscription.

Thus, Chandler’s insight exposes the spreadsheet as a reality filter. It reifies the calculable, delegitimizes the unquantified, and normalizes visibility within a structured symbolic grammar. In a spreadsheet, data becomes visible only if it can be quantified, categorized, or calculated. What cannot be structured — emotion, ambiguity, contradiction — is rendered invisible in the abstraction process. A spreadsheet template enforces schema over substance: even subjective data must be shaped to fit predefined cells (e.g., “Employee Satisfaction: [1–10]”). Sign systems do not passively reflect reality. They construct it through symbolic choices and structures.

Lucian Bagiu’s analysis of structuralism vs. deconstruction illuminates how symbolic systems determine what can be made visible.[7] The spreadsheet, in this light, does more than represent data. It functions as an epistemological technology that enforces a regime of visibility. What appears in the cell is not “truth” but truth-as-structured-by-symbols. Conditional formatting, pivot tables, and data validation create layers of symbolic control, filtering what is emphasized, hidden, flagged, or acted upon. Bagiu helps us see that this is not a neutral process. It is an act of symbolic governance. The spreadsheet decides what counts as knowledge, what is ignored, and what is actionable.

Summary

This blog post argues that spreadsheets, dashboards, and AI models are not neutral tools but powerful “semiotic infrastructures” that actively construct and govern reality. It introduces two key concepts to explain this process: “writing-as-substitution” and “computation-as-symbolic-action.”

Drawing on the semiotic theories of Jacques Derrida and Charles Sanders Peirce, the post explains that a spreadsheet cell doesn’t just hold data; it substitutes a simple symbol for a complex, absent reality. This number or text is a “trace” that makes the world calculable by stripping away its messy, unquantifiable details.

The post then describes how formulas and functions act as a form of “computation-as-symbolic-action.” A formula like =SUM or =IF is not just a calculation but a script that automates a decision or judgment based on these symbolic traces. This process creates a chain of meaning where value is determined by its relationship to other cells, always deferring to absent data.

Ultimately, it concludes that these technologies function as systems of governance. By defining what can be measured and calculated, the spreadsheet filters reality, rendering things like emotion or ambiguity invisible. It doesn’t just reflect the world; it replaces, structures, and enforces a specific, calculable version of it.

Citation APA (7th Edition)

Pennings, A.J. (2025, Sep 03) Peirce and Derrida on the Logic and Power of Spreadsheets and Dashboards. apennings.com https://apennings.com/technologies-of-meaning/peirce-and-derrida-on-the-logic-and-power-of-spreadsheets-and-dashboards/

Notes

[1] This was a real baseline post for me although I only finished it recently. While reviewing a paper on spreadsheets in mid-July 2025 that was written with Patrick Rose, I ran across a mention of “substitution.” This started a whole new trajectory of inquiry that resulted in the conceptualization of the SACT stack.
[2] Peirce, C.S. (1955). Philosophical Writings of Peirce. Dover. Derrida, Jacques. Positions. Translated and annotated by Alan Bass. University of Chicago Press, 1982.
[3] Peirce, C.S. (1955). Philosophical Writings of Peirce. Dover.
[4] Chandler, Daniel (1995). The Act of Writing: A Media Theory Approach. Aberystwyth: University of Wales. Also see Chandler, D. (2002). Semiotics: The Basics. London: Routledge. Chandler built one of my favorite websites on semiotics. I use it in my Visual Rhetoric and IT class.
[5] F. de Saussure. 2nd trans.: Roy Harris, trans. (1983) Course in General Linguistics. La Salle, Ill.: Open Court.
[6] Derrida, J. (1976). Of Grammatology. Johns Hopkins University Press. Available at https://bit.ly/3L0yAKu
[7] Bagiu, L. (2009). Writing in Deconstruction vs Speech in Structuralism (Jacques Derrida vs Ferdinand de Saussure). Transilvania, XXXVII (CXIII)(8), 79-87. http://www.revistatransilvania.ro/nou/ro/anul-editorial-2009/doc_download/26-numrul-82009.html

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Anthony J. Pennings, PhD is a professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy, visual rhetoric, and engineering economics. From 2002-2012, he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, digital spreadsheets, enabling frameworks, enterprise systems, financial technology, meaning makers, technologies of meaning, visual literacy and rhetoric
Tags: AI > Algorithmic Governance > Charles Sanders Peirce > computation-as-symbolic-action. > Dashboard > Derrida > Différance > Interpretant > Jacques Derrida > Peirce > Peircean interpretants > Semiotics > spreadsheet > Trace > triadic semiotics > writing-as-substitution

Analyzing the Economics of Asymmetric Robotic Drone Warfare

Posted on | September 1, 2025 | No Comments

I’ve been intrigued by the asymmetric conflicts in Ukraine and Yemen these last few years. The introduction of low-cost drones and missiles has threatened established powers and the protection of global maritime trade. They are increasingly important topics in my classes, especially Engineering Economics, ICT for Sustainable Development, and Sensing Technologies for Disaster Risk Reduction.

Asymmetric warfare is conflict between two opposing sides with significantly different military capabilities, power, and tactics. It often involves weaker forces employing unconventional methods, such as guerrilla tactics, terrorism, or hit-and-run attacks, to exploit the strengths of their powerful adversary, thereby prolonging the conflict and challenging traditional warfare norms.

While this type of conflict typically involves a state actor versus a non-state actor, such as insurgents or resistance militias operating within controlled territory, the development of robotic drone-based warfare suggests scenarios that will likely continue to pit smaller state actors against larger ones (asymmetric) with established legacy military capabilities.

The major categories of robotic drones in warfare are best understood by their operational domains, such as air, land, sea surface, and underwater, and then by their primary mission function. These systems range from small, hand-launched vehicles to massive autonomous ships and submarines, fundamentally reshaping the modern battlefield. They are organized in the following categories:

Uncrewed Aerial Vehicles (UAVs) / Air Drones
Uncrewed Surface Vessels (USVs) / Sea Drones
Uncrewed Underwater Vessels (UUVs) / Submarine Drones
Uncrewed Ground Vehicles (UGVs) / Land Drones
Swarms and AI-Driven Systems

The best way to research the economics of asymmetric warfare using robotic drones is to employ a Cost-Imposition Framework. This approach analyzes the conflict not just as a military engagement, but as a form of economic warfare where the primary goal of the weaker actor is to force the stronger actor to spend a disproportionate amount of resources on defense.

The research should be structured into at least three parts. One is analyzing the inferior actor’s costs, the second addresses the superior actor’s costs, and then finally modeling the interaction between the two actors.

Implementing a Cost-Imposition Framework

At its core, the economic asymmetry in drone warfare is about imposing unsustainable costs on the adversary. A weaker actor uses extremely low-cost, often commercially available drones to create damage and threats that extremely high-cost, sophisticated defense systems must counter.

The key metric is the cost-exchange ratio. This ratio compares the attacker’s cost to achieve an effect versus the defender’s cost to prevent it. It measures the incremental cost to the aggressor of obtaining one additional offensive device that bypasses a defense shield. A relevant example is a $1000 UAV modified to carry a grenade, forcing a state military to fire a $2 million air defense missile to intercept it. This attack results in a catastrophic economic loss for the defender, even in the event of a successful interception.

Part 1 – Economics of the Inferior Actor (The Attacker)

The research should quantify the full “attack stack” of coordinated vehicles of the weaker side, which is defined by its overall low cost, adaptability, and strategic impact. Accounting should encompass the full range of unconventional weapons available, including guerrilla warfare, terrorism, cyberattacks, and other methods that exploit a superior opponent’s weaknesses.

This research would expand into human capital, procurement and supply chains, and research and development. Human capital involves quantifying the low cost of training operators, who often only require skills comparable to those of a video gamer, and the minimal organizational overhead of deploying decentralized cells. Procurement and supply chain dynamics encompass analyzing the costs of sourcing commercial-off-the-shelf (COTS) drones from platforms like AliExpress, 3D printing components, and adapting simple munitions. Research the illicit supply chains used to acquire these parts.

Research and development is another crucial area to examine. The “R&D” is often just rapid, low-cost tinkering and experimentation. Failure is relatively inexpensive, allowing for a significantly faster military innovation cycle where new technologies, tactics, and organizational methods are developed, tested, and adopted, progressing from conceptualization to widespread implementation and eventual obsolescence.

Part 2 – Economics of the Superior Actor (The Defender?)

The research must then analyze the defender’s cost structure, which is often characterized by high expense and slow adaptation. This examination would encompass procurement and operational costs, research and development, as well as indirect costs.

Procurement and operational costs include the multi-billion dollar price tags for dedicated air defense systems (radars, interceptors like the Patriot or Iron Dome) and the high cost of each interceptor fired. It also includes the personnel and maintenance costs required to operate these systems 24/7.

“R&D” in this case involves the massive, multi-year, and often trillion-dollar state investment in developing specialized counter-UAV technologies like lasers, directed energy weapons, and AI-driven detection systems.

Indirect economic costs can involve critical variables. The model must include the massive secondary costs of a successful or even an attempted attack, such as the economic impact of shutting down an airport, damaging critical infrastructure like a bridge or oil refinery, or the cost of deploying security measures to protect civilian areas.

Part 3 – Modeling and Synthesis

The final step is to synthesize the data into a comparative model to answer key economic questions. This synthesis involves data collection and financial modeling. Gather real-world cost data from recent conflicts in Ukraine, Syria, and Yemen for specific drone technologies and the interceptors used against them. Build a modelling system that allows a comparison of the cost-to-attack versus the cost-to-defend under various scenarios.

Summary and Recommendations

This post describes how asymmetric warfare, a conflict between actors with vastly different capabilities, is being reshaped by robotic drones. It proposes a Cost-Imposition Framework to analyze this conflict as a form of economic warfare. The core idea is that a weaker actor can use cheap, adaptable, and commercially sourced drones (across air, land, and sea) to force a militarily superior actor to spend disproportionately large sums on high-tech defenses.

The key metric for this analysis is the “cost-exchange ratio,” which compares the low cost of an attack (e.g., a $500 drone) to the high cost of defense (e.g., a $3 million missile). The research involves a three-part process: analyzing the low-cost structure of the attacker, the high-cost structure of the defender, and finally, synthesizing the data in a comparative model.

Suggested Model for the Final Step

An effective model to fulfill the final step of synthesizing the data and answering the key research questions is an Agent-Based Model (ABM). An ABM is a computational simulation that models the actions and interactions of autonomous agents (both individuals and collective entities) to see what effects they have on the system as a whole. It is suited for this problem because it can directly simulate the economic and tactical interplay between numerous, distinct assets.

Citation APA (7th Edition)

Pennings, A.J. (2025, Sep 1) Analyzing the Economics of Asymetric Robotic Drone Warfare. apennings.com https://apennings.com/digital-geography/analyzing-the-economics-of-asymetric-robotic-drone-warfare/

Note: Chat GPT and Gemini were used for parts of this post.

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Not to be considered financial advice.

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Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and engineering economics. From 2002-2012 he taught comparative political economy, digital economics, and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: automatrix, digital geography, mobile technologies, sensing technologies
Tags: asymmetric warfare > cost-exchange ratio > Cost-Imposition Framework > military innovation cycle > Uncrewed Aerial Vehicles (UAVs)

LSEG Workspace as the Central Spreadsheet Grid for Eurodollars

Posted on | August 23, 2025 | No Comments

In spreadsheet capitalism terms, LSEG’s Workspace is a major platform that continues to turn fragmented eurodollar dealing into a coordinated, cell-based, time-stamped matrix of quotes and trades.

In grid infrastructure terms, Workspace is fast becoming the organizing interface where eurodollar rates, FX swap points, and Treasury collateral pricing are aggregated and computed. Surprisingly for people who do not understand how the US dollar worldwide, it functions as a key node in the daily digital grid for offshore USD liquidity.[1]

Workspace is spreadsheet logic made infrastructure, and this post outlines the major functions + formulas that make it the newest gridmatic trading infosystem for eurodollar markets. It is part of my “formulating power” project that looks at how spreadsheet formulas have not only made spreadsheets like Excel important, but how the grid-like structure and symbolic computing has expanded into a wide variety of information-based services.

Reuters Dealing (1980s–1990s) was the first electronic dealer-to-dealer chat and price system, effectively the “proto-grid” for eurodollar spot/forward trading. Thomson Reuters’ Eikon (2000s–2010s) evolved into a spreadsheet-like terminal with live market data, analytics, and FX integration. LSEG Workspace (2020s–) emerged as the rebranded, cloud-based successor after Refinitiv’s integration into the London Stock Exchange Group (LSEG). Workspace is not entirely new, but the next-generation layer in the same “gridmatic” tradition.

But it is not exclusive, it coexists with the Bloomberg Terminal and BlackRock’s Aladdin. The Bloomberg “Box” is still dominant on many trading desks, and Aladdin structures the portfolio-level balance sheet exposures. Workspace is strongest where FX and money markets plus data grids converge. Thus, Workspace is becoming the eurodollar spreadsheet of record for traders and banks.

The eurodollar market relies on offshore USD deposits, lending, and collateralized borrowing, and its infrastructure requires:

Real-time USD funding rates (OIS, SOFR-linked, FX swap basis).

Collateral pricing (USTs, agencies).

Cross-border FX settlement data (via SWIFT/CLS).

Workspace pulls all of these streams together:

FX Dealer Chat and Matching is the modern version of Reuters Dealing and still where traders text and eurodollar liquidity is posted/negotiated.

Data Grids: yield curves, swap points, repo rates, and collateral haircuts displayed in table form, live-linked to Excel or APIs.

Analytics: symbolic computing overlays for stress-tests, correlations, and risk across currencies, but USD is always the pivot cell.

The Gridmatic Infrastructure of Workspace

Workspace is a giant spreadsheet-as-interface consisting of data tables, formula-driven analytics, and live-linked cells. Application Programming Interfaces (APIs) that connect to Excel, Matlab, and Python, or the combination (Excel/Matlab/Python) turns Workspace into a grid feeder for downstream symbolic computing in popular spreadsheets. The collaboration grid replaced the “chat window + quote” of Reuters Dealing (a real-time messaging tool for financial professionals) where the trader/analyst views the same USD-centered tables.

So, Workspace turns fragmented eurodollar dealing into a coordinated, networked interface, which is a cell-based and time-stamped matrix of quotes and trades. Workspace is becoming the organizing gridspace where eurodollar rates, FX swap points, and Treasury collateral pricing are aggregated and computed in a networked spreadsheet for offshore USD liquidity.

LSEG Workspace and Its Implications for USD Network Power

By now owning Workspace and Refinitiv FX platforms plus popular benchmark indices (FTSE Russell), LSEG positions itself as the non-US hub for eurodollar telematics. Yet because everything inside Workspace still references US Treasuries and Fed policy rates, the USD remains the anchoring cell of the eurodollar spreadsheet, even on a “foreign” platform. Workspace (LSEG), but with Bloomberg, and Aladdin, overlap and compete in anchoring the USD/eurodollar system, but with Treasuries still in the central reference cell.

LSEG Workspace is spreadsheet logic made infrastructure. Its major functions + formulas make it the newest gridmatic spreadsheet system for organizing eurodollar markets. The major functions and formulas used by Workspace work as data feeds and grid cells. Workspace ingests real-time price streams into cells that look and behave like spreadsheet ranges.

=PRICE(UST_10Y, NOW()) – live U.S. Treasury yield.

=FXSPOT(EURUSD) – spot euro-dollar exchange rate.

=OIS(USD, 3M) – overnight indexed swap rate.

=BASIS_SWAP(EURUSD, TENOR=3M) – FX swap basis points.

These are the “input cells” that anchor all calculations.

Curve Construction & Interpolation

To model funding markets, Workspace builds continuous yield curves from discrete data.

=YIELD_CURVE(INSTRUMENT=UST, METHOD=SPLINE) – fits Treasury yields across maturities.

=BOOTSTRAP(SOFR_SWAP, TENORS) – constructs discount factors from SOFR swap rates.

=INTERP(RATE, MATURITY=7D) – interpolates between tenor points (e.g., repo rate curve).

These are crucial for eurodollar term lending, where pricing depends on forward curves.

Collateral & Repo Analytics

Eurodollar mechanics are collateral-driven so Workspace handles haircut and repo pricing with the following.

=REPO_RATE(COLLATERAL=UST_2Y, TERM=ON, COUNTERPARTY=PD) – overnight repo rate for a 2Y Treasury.

=HAIRCUT(ASSET=UST_30Y, CCP=LCH) – margin requirement for long bond collateral.

=RRP_RATE(TERM=ON) – Fed’s overnight reverse repo program rate, the floor of dollar funding.

These cells define the “funding grid.”

FX & Cross-Currency Funding

Workspace integrates FX swaps, which are the offshore synthetic dollar creation mechanism.

=FXSWAP(EURUSD, TENOR=1W) – implied cost of borrowing USD via FX.

=COVERED_IRP(USD, EUR, TENOR=3M) – checks covered interest parity (CIP).

=BASIS(EURUSD, 3M) – eurodollar “basis spread,” measuring stress in offshore dollar lending.

This function set is where the eurodollar meets FX.

For banks and dealers, Workspace provides grid-like portfolio functions for balance sheets and capital allocation.

=VaR(PORTFOLIO, CONF=99%) – Value-at-Risk of USD positions.

=LCR(LIQUID_ASSETS, NET_OUTFLOWS) – liquidity coverage ratio.

=BALANCE_SHEET(CURRENCY=USD, ASSETS-COLLATERAL, LIABILITIES-FUNDING) – structured balance sheet grid.

This mirrors how eurodollar institutions literally tabulate their offshore books in grid form.

Policy & News Integration

Financial news is also tied right into the spreadsheet logic.

=FOMC_TARGET_RATE() – current Fed funds target.

=ECON_RELEASE(CPI_US, DATE) – inflation print automatically populates.

=NEWS_SENTIMENT(FED, WINDOW=1D) – text mining of Fed statements, outputting a score.

This allows policy shocks to instantly cascade through formulas across FX, repo, and swaps.

Optimization & Allocation

Workspace incorporates optimizers, which are like Solver in Excel:

=ALLOCATE(FUNDING, CONSTRAINTS={DURATION<2Y, COSTEurodollar-Specific Grid Structure

If you were to visualize Workspace as a spreadsheet, its eurodollar tabs would include:

Tab 1 – Market Inputs: Treasuries, SOFR, repo, FX spot.
Tab 2 – Funding Curves: OIS, cross-currency basis, term structure.
Tab 3 – Collateral: Haircuts, repo spreads, RRP anchors.
Tab 4 – Balance Sheet: Offshore USD assets/liabilities in grid form.
Tab 5 – Optimizer: Allocation solver for cheapest-to-deliver funding.
Tab 6 – News/Policy: FOMC targets, Fedwire flows, central bank interventions.

Conclusion

LSEG’s Workspace is the newest gridmatic infrastructure for the eurodollar system. It transforms fragmented offshore USD trading into a cell-based, time-stamped spreadsheet interface, where Treasury yields, FX swaps, and repo collateral are continuously aggregated and recalculated.

LSEG Workspace works like a giant live spreadsheet that organizes the eurodollar system into cells and formulas. Treasuries and Fed rates are the anchor cells. Repo, FX swaps, and offshore deposits are formula-driven references. Optimizers and risk tools act like Solver, wiring symbolic computing into the market grid.

The “gridmatic infrastructure” of Workspace is what allows offshore USD lending to stay synchronized with Fed policy and Treasury collateral — keeping the U.S. dollar anchored as the central cell of global finance.

Not intended to be investment advice of any kind.

Notes

[1] I started my research on eurodollars for my MA thesis in 1986 that investigated how deregulation in finance and telecommunications were allowing new financial services to emerge like SWIFT, CHIPS, and Reuters Monitor and Dealing. I continued to write about electronic money using the term digital monetarism but drawing on my PhD, what we call digital monetarism is better understood as spreadsheet capitalism: a system where gridmatic infrastructures (Bloomberg, Aladdin, Workspace) continuously synchronize global liquidity, collateral, and pricing to keep the U.S. dollar at the core of world money.
[2] Chat GPT was an important research aide in this inquiry. In accordance with the determiniations set by the US Copyright Office, I claim the results of prompts as property. I guide the inquiry and carefully edit any LLM returns. Prompt 1: Does the USD operate globally with network effects?
Prompt 2: The US dollar’s global power is not just about US GDP or military strength. It’s about network effects built into financial infrastructure, where the dollar sits at the center cell of the global spreadsheet, anchoring liquidity, collateral, and pricing. Continuing the analysis of spreadsheet capitalism elaborate on how technological systems like BlackRock’s Alladin, Bloomberg’s “Box” and Thomson Reuter’s Eikon work with treasury markets, foreign exchange trading, financial news, and central banks to keep the USD anchored as the world’s primary reserve and payment system.
Prompt 3: Is LSEG Workspace the new central gridmatic organizing spreadsheet system for the eurodollar market?

Citation APA (7th Edition)

Pennings, A.J. (2025, Aug 23) LSEG Workspace as the Central Spreadsheet Grid for Eurodollars. apennings.com https://apennings.com/technologies-of-meaning/lseg-workspace-as-the-new-central-spreadsheet-for-eurodollars/

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Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, characteristics of digital media, crisis communications, data analytics and meaning, democratic political economies, digital coordination, digital geography, digital media economics, digital media management, digital spreadsheets, disaster risk management, disruption, electric money, enabling frameworks, financial news, financial technology, geopolitical risk, global communications, smart new deal, strategic communications, sustainable development, technologies of meaning, technology management, telegraphic political economy
Tags: Bloombery Box > eurodollar > LSEG Workspace > Thomson Reuters Eikon > USD liquidity

Excel vs. Java: Two Languages, Two Worlds of Thought: Symbolic Grid vs. Algorithmic Script

Posted on | August 12, 2025 | No Comments

Spreadsheets are often dismissed as “just” tables with lots of numbers. Still, their symbolic computing functions operate in a very different register than traditional programming, and that difference is why they’ve been so powerful in business, finance, and administration.

In this post, I examine the differences between Excel spreadsheets and traditional programming, primarily Java, but with some references to Python. Other computer languages like C and Fortran could be included but that would make the analysis too complex. My major objective is to explore the unique symbolic computing capabilities of the spreadsheet.

This inquiry comes from my long history into inquiring into both areas of computing, mostly spreadsheets. But also my more recent experience in a program that stresses students learn Java and Python. It ends with some speculation about AI and its influence on both.[1]

Excel vs. Java/Python

When we talk about programming, it’s easy to imagine a single tradition — algorithms, variables, loops — the kind of logic you find in computer science textbooks. But spreadsheets operate in a parallel tradition. They use symbolic computing in a grid environment. And when you set Excel side-by-side with Java and Python, the contrasts reveal not just different tools, but two very different ways of thinking about computation, knowledge, and work.

Excel and Java are representatives of two cultural histories of computing. The ledger-turned-simulator, serving the micro-decisions of commerce and finance. And the algorithm-turned-platform, serving the macro-architectures of global software development. Both shape our world, but through different epistemologies, and in service to different forms of power.

Excel is declarative and reactive. You don’t tell Excel how to compute something step-by-step; you state the relationship between values — =SUM(A1:A10). Then let the spreadsheet engine figure out the rest. Users declare relationships between cells; the engine maintains them automatically. The primary metaphor for the spreadsheet is the ledger. Spatial placement and references produce meaning. The “program” is a living grid where changes ripple through automatically with updated information.

Java’s procedural lineage grew from Cold War software engineering. It is procedural and object-oriented. You write explicit instructions: declare variables, loop over arrays, branch with conditions. The “program” is a designed machine — it does nothing until you start it, and it runs in the sequence you prescribe. Likewise, Python is a multi-paradigm programming language that is imperative, procedural, and mostly object-oriented (with functional elements). Users specify step-by-step instructions in human-readable syntax. The primary metaphor is the script, a narrative sequence of commands to be executed.

Python was developed in the early 1990s in the tradition of general-purpose scripting languages like Unix shells and C. It was designed for readability, minimal syntax noise, and broad applicability — from system scripting to AI/ML. Python code runs from top to bottom, unless redirected by flow control. Scripts can be interactive (REPL/Jupyter) or batch-run, but they start from a blank state each run unless persisted.

Excel’s grid is both interface and execution environment. A cell like “B2” is a symbol in a two-dimensional space, not a memory address. Ranges (A1:C3) are symbolic collections, defined by their position in the grid. The layout is the program. In Java, memory is abstract and invisible. You construct arrays, lists, and objects, but they live behind the scenes. The UI, if you build one, is separate from the computation. The layout of your code is not the same as the data it processes.

In Excel, execution is continuous and event-driven. Change one value, and the dependency graph kicks in: only cells that rely on that value are recalculated. There’s no “Run” button for formulas — the sheet is always “on.” In Java, execution is discrete. You compile and run the program, it executes a defined sequence, and it stops. Event-driven behavior (like GUIs) is possible, but it must be explicitly coded with listeners and handlers.

In Excel a cell can hold a number, text, a boolean, or an error without predeclaring its type. This flexibility makes rapid modeling possible, but it also hides data-type errors until they matter. In Java, every variable must have a type before it can be used, and the compiler enforces it. This prevents certain categories of bugs but slows the fluid improvisation that spreadsheets enable.

The spreadsheet user thinks spatially — “This cell depends on that one; those three cells add up to this total.” Programming in Excel feels like maintaining a ledger or building a map — computation embedded in geography.

The Java programmer thinks algorithmically — “First I do this, then I do that.” Programming in Java feels like designing a machine — computation as a process unfolding in time, not space.

Economic and Cultural Lineages

Excel descends from accounting, tabulation, and ledgers. It carries the epistemology of the bookkeeper — numbers aligned in rows and columns, meaning attached to placement, relationships visible at a glance. Java descends from structured programming and software engineering. It carries the epistemology of the engineer — modularity, encapsulation, and abstraction layers designed to scale from small programs to global systems.

Why it matters? Spreadsheets turned computation into a cultural form accessible to non-programmers, embedding symbolic computing into the everyday life of business, government, and finance. Java, and languages like it, built the backbone of global software infrastructure. Both changed the world — but they changed different worlds.

Excel’s “gridmatic” logic empowers rapid prototyping and live simulation, perfect for domains like finance where decisions are model-driven and iterative. Java’s “narrative” logic excels in building robust, reusable systems where execution order and data integrity are paramount.

The Role of AI

Excel’s symbolic, reactive grid and Python/Java’s procedural, text-based scripts represent two mature modes of computation. AI — especially large language models (LLMs) and generative systems — introduces a third mode: probabilistic, pattern-driven inference.

Instead of Excel’s symbolic mapping (“Cell B2 = sum of A1:A10”) and traditional programming’s procedural instruction (“Loop through this array and sum the numbers”), we now get pattern completion (“Given these values and my training data, here’s the sum, the trend, and a prediction — without you explicitly defining the steps”). AI doesn’t just run code — it generates it, interprets messy inputs, and adapts behavior without explicit reprogramming.

Excel is already being transformed into a natural-language modeling surface with Copilot for Excel that lets you type “Show me sales trends for Q3” and get formulas, pivot tables, or charts without knowing the syntax. AI can generate whole spreadsheets from plain text or PDFs, eliminating manual cell-by-cell building. Predictive modeling (e.g., forecasting, anomaly detection) is moving inside Excel’s grid without the user writing formulas.

AI is turning Excel into “spreadsheet as conversation.” The grid remains, but much of the symbolic formula-writing is offloaded to an AI intermediary. Excel’s tabular form will survive because the grid is a powerful cognitive interface. But its internal programming layer will be increasingly AI-written rather than human-written.

For procedural languages, AI is already functioning as a code generator where LLMs write Python/Java from natural language prompts.
It also works in refactoring and as a debugging assistant — turning vague goals into structured, tested, deployable code. AI can be seen as an Integration orchestrator, pulling APIs, databases, and machine learning models together without the developer having to remember all syntax and library calls.

Python in particular is symbiotic with AI — it’s the dominant language for training and deploying models, and AI frameworks (PyTorch, TensorFlow) are Python-native. Java will continue in large-scale systems and enterprise software, with AI increasingly writing parts of it (e.g., data-access layers, boilerplate). Procedural coding won’t vanish — but the human role will shift from “writing code” to “specifying requirements, constraints, and reviewing AI output.”

Excel democratized computing for business users without replacing procedural programming. It just absorbed a huge chunk of modeling work. AI will do the same. It will democratize and accelerate both symbolic and procedural programming, not erase them.

AI will continue to replace the rote coding of standard programming patterns, but humans will still be needed to define problems precisely, decide trade-offs between performance, accuracy, and interpretability, and ensure compliance, security, and business alignment.

Conclusion

Spreadsheets like Excel embody a distinct symbolic computing tradition, separate from conventional programming languages such as Java. Excel is declarative, reactive, spatial, and loosely typed, with computation embedded directly in its two-dimensional grid. Java is procedural, algorithmic, temporal, and strongly typed, with computation separated from its interface.

These differences reflect their cultural and economic lineages. Excel emerges from accounting and tabulation, enabling rapid, accessible modeling for business and finance, while Java descends from structured programming, supporting large-scale, robust software systems. Both have profoundly shaped modern life, but in different domains and through different epistemologies.

Understanding these perspectives side-by-side isn’t just academic. It’s a reminder that “programming” isn’t one thing — it’s a family of practices, each with its own history, cultural lineage, and vision of what computers are for. Digital spreadsheets will continue to be used because it’s a human-readable canvas for understanding and auditing results.

Excel and Python/Java will increasingly become AI-mediated layers in a bigger computational infosystem. Excel will be the visual decision surface while Python/Java are the execution engines and integration layers. AI will be the translator and generator between human intent and machine implementation.

Notes

[1] I wrote my MA thesis on eurodollars and telecommunications deregulation but didn’t appreciate the importance of the spreadsheet until my PhD student days, and my personal experience running a small Center for Modern Media at the University of Hawaii in the summers during graduate school. My overriding purpose is to develop a more sophisticated understanding of the digital spreadsheet and its impact on finance and other social areas.
[2] Many thanks to my Stony Brook colleague at SUNY Korea Antonino N. Mione who teaches the Java course for reviewing this article. Any remaining mistakes are strictly mine. He made a interesting point that I have not integrated into the text yet. “You can write a spreadsheet application in Python or Java, but it would be very hard to develop a Python interpreter or Java compiler simply using an Excel application.”

Prompt: I used a very general ChatGPT prompt to get this conversation started. Describe the symbolic computing functions in spreadsheets and how they differ from traditional computer science concepts. Then I followed up with requests to include Java and Python.

Citation APA (7th Edition)

Pennings, A.J. (2025, Aug 12) Excel vs. Java: Two Languages, Two Worlds of Thought: Symbolic Grid vs. the Algorithmic Script. apennings.com https://apennings.com/meaning-makers/digital-spreadsheets-power-over-people-and-resources/

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Anthony J. Pennings, PhD is a professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

Category: artificial intelligence, creative industries, cultural Industries, data analytics and meaning, democratic political economies, digital coordination, geopolitical risk, political economy of media, science and technology studies, technologies of meaning, technology management, telecom policy and net neutrality

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