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The Internet Has a Pulse, and This Sequencer Lets You Hear It

by Indie designer - Harish Pillai13mApril 10th, 2025

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The social sequencer is a new media synth interactive art piece to explore the concept of the emergence theory through sound.

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The social sequencer is a new media synth interactive art piece to explore the concept of the emergence theory through sound. It is a reflection of how systemic properties or behaviors "emerge" from the dynamic interactions of components in a system and the nature of what changes and what does not. It’s an interactive thought experiment as a web app on what contributes to patterns created in systems from interactions. Imagine social data as the town hall bell except there are more tones involved, action triggers a drumbeat that blends with others comments into a symphony. It’s a conversation on contextual expression of data forms/patterns through data sonification.

Technical Framework (WAV vs. MIDI)

This project visualizes how collective user interactions create unpredictable patterns, comparing fixed WAV data with dynamic MIDI instructions as metaphors for complex adaptive systems in nature, economics, and digital networks.

WAV format

The data of a sound is captured in a place and time with certain types of technology reflective of the potential of capturing a period in history. WAV is digital but is an analog waveform, it can be reinterpreted through audio effects processing, but the actual sound or data snapshot remains.

On a societal level, the audio data snapshot might exist as an objective truth but is subjected through the lens of the viewer through interpretations and context like a signal running through an effects processing. It sounds different with each effect but that sound was captured hopefully accurately in place with the right working equipment to preserve the accuracy of the data signal/form.

MIDI format

MIDI like WAV is fully digital, but unlike WAV is data of instructions, almost like air, is it like the DNA? The genetic information stored in an organism's DNA contains the instructions for all the proteins the organism will ever synthesize. It controls how sounds are synthesized and is not fixed in terms of its properties as it is dynamic controlling how a synthesizer generates sounds. It is more flexible than WAV in being able to modify the timing, notes and instruments while WAV can only be changed with effects. MIDI is a set of instructions that tell a synthesizer what notes to play, how long, and with what velocity giving infinite variations from the same data.

WAV vs MIDI

To compare both MIDI and WAV, WAV is a fixed form or fixed data signature of a painting and its subject matter and aesthetics is defined by the parameters of its time. It can be reinterpret by different audio effects and takes on a new meaning. MIDI is dynamic data signature musical score where you change the instruments, tempo, and how it's played dynamically and produce different sounds.

Design

This synthesizer has these qualities that it utilizes various agents who are the users through networks that trigger the instruments and audio effects to produce complex feedback loops from small changes through user engagement. The emergent system is self-organizing as small changes triggered by the instrument from interactivity of users to produce continuous music. The context is determined by specific cues of musical triggers of which audio effect is interacted by the user and MIDI signals funneled through social engagement considered as signals from the system.

Let’s talk about the sequencer. It’s sort of like a structural container to see possible permutations. Is this a good feature to talk about emergence? The decision to consider a sequencer would be a classic component in a synthesizer, still a sequencer is a type of container or a structure to see possible permutations. It organizes the data or information in way that can be perceived as ordered events. It is a framework that works with predefined structure. If the conditions of the system is too rigid then there is a tendency for preset patterns instead of something emergent that is drawn by unexpected behaviour.

A flexible sequencer model facilitate social interactions to create structured, evolving patterns without locking them into rigid, pre-defined beats or measures. Instead of a fixed grid, it adjusts its timing, density, and playback rules dynamically based on engagement levels. The sequencer is always evolving and unpredictable.

How does that work to simulate the concept of emergence? The sequencer starts empty, as new social interactions occur, patterns that become steps are added. If interactions slow down, steps fade out preventing repeating loops. The interactions use engagement to trigger different instruments as overlapping interactions create harmonies which are emergent behaviours from interactions.

The system remembers previous interactions and occasionally reintroduces them in later sequences as patterns arise naturally from social behavior, rather than pre-planned rules.Pre-set patterns could fade out naturally or be brought back as an echo if similar interactions occur again. The interaction of user engagement trigger different instruments from shares that stretch sequences to introduce polyrhythms. Engagement by comments create patterns less predictable and likes can increase reverb or delay notes that effect audio synthesis through a user’s control.

It’s the soundtrack of the internet being played in real time by engagement of users. The sequencer is in continuous change and being unpredictable as users collaborate to shape the music without written arrangement. The control element is informed by the agency of the user which is from the WAV generator. The user is able to generate sample WAV file to load into the custom samples panels, after doing so they are able to use the WAV effect controller to change the properties of the sound with reverb, delay and feedback. If they decide not to, they can still use the WAV button and use the default mode to play the beat and change the sounds with the WAV effects. The MIDI button receives the data signals from the social media engagement and plays the sequence pattern it receives.

Implications

If this project had been connected to actual social media data it would be interesting to see how patterns in social data might form, are there repeat data patterns? Users would indirectly collaborate in shape the music, without coordination. The music sequencer might have endless supply of patterns to create the music and it would be difficult to hear the highs and lows, but could the analytics show us anything that points to breaking patterns and forming new ones? Or is it everything cyclical? As mentioned previously MIDI is dynamic data in that it is more perceptible to be altered. The WAV files are reflective on the quality of the data files, it can be manipulated but its origin is still static in conception. This concept uses a combination of MIDI and WAV to illustrate the concepts and hear the difference or lead into a conversation on how they differ.

It’s interesting to notice that the WAV option has 4 files that you could generate and use the effect processor to hear a variety of tones, and this demonstrate how small patterns of structure can combine to form a variety expressions or you can say data signatures. Steve Reich explored minimalism in his work which was to utilize repeating motifs and loops that make up harmonies. The WAV section lives up to this concept because with 4 WAV files you are able to derive a variety of tones through the controls of the audio effects is controlled by user which relies on their decision.

When combine with MIDI and WAV you have a full expression of the audio as overlapping interactions can create unexpected harmonies and rhythms. If the user decides to upload their custom WAV files the tonality can be impacted by the quality of the audio file. Was the sonic quality captured accurately? The data representation is if a piece of history was captured well.

The sequencer let’s us observe the way we think about possible combinations of the information interacting to produce data or output. There are a few criteria of emergence when it comes to how simple interaction lead to expression focus on decentralized, non-linear, and self-organizing interactions, and while emergent systems may not have a fixed structure, the structures that do form are critical to defining and stabilizing the new behaviors that arise.

The role of structure is to facilitate the organization of the interactions, making the emergent pattern recognizable. Emergent phenomena typically display some form of structure, though it might be transient, fluid, or evolving rather than rigid. Even if the structure is not permanent, it provides temporary stability and a framework that allows the system to adapt and evolve. If you want the elevator pitch it’s this the basic elements or permutations might be limited, their interactions can yield rich, emergent structures that are not directly apparent from the individual parts can produce complexity.

Emergence isn’t just about having an infinite number of permutations; it’s about how simple interactions lead to novel, often unexpected, behaviors and patterns. Overall if a system is a universal cosmic instrument ringing out music, do patterns repeat, do you hear the same motif, does it have a memory? Does a signal ring once and go out in a whimper never to repeat again? Let’s explore how this applies to broader systems.

Philosophical Questions: Social sequencer as a view into Systems

This thought experiment tries to ask, are variations of data signatures bound by permutations for expressions? What combination of data and information produces an expression and is it bound by periods of history given the sentiments and progression of technological and information advancement. Does technological advancement break from the permutations of the sequencers or is bound by the restriction of structures of history. Does each historical period express based on a convergent central theme that determined its capacity for technical jumps? Is it the sums of the parts, or its one determining factor?

The social synth is a view into adaptive systems and how interactions exhibit emergent behaviours from decentralized decisions from users who provide input data in a sequence of time. Emergence is when complex behavior arises from simple interactions, without a central controller. This model could be compared to how economic free market might work or how democratic nations function through policies which are meant to set a foundation for a nation to flourish, policies could be viewed as the structure from which the interactions of the system produce a pattern. Using a musical instrument plugged into data as view in how a system behaves gives us the possibility to ask larger questions.

This new media social synthesizer provides a grounding to demonstrate the concepts of emergent theory which are new behaviors from systems as a result of interactions from collective phenomena that display unique qualities that individual elements do not. Interactivity through various agents produce unexpected outcomes reflecting complex feedback loops. Small changes in the system in a non-linear approach produce large effects at systemic level.

Context is determined by specific properties of the system and its environment at a given time. Emergent systems exhibit self-organizing properties, where order and structure arise spontaneously without external control or centralized planning. Emergence results in adaptive systems that generate new forms of behavior or organization in response to environmental changes.

Emergent theory is the study of complex systems that describes how novel properties, behaviors, or patterns arise from the interactions of simpler components. Properties that cannot be fully predicted or explained by analyzing the parts in isolation. In system biology, emergent properties arise when molecular interactions (e.g. gene regulation, protein networks) produce complex behaviors like homeostasis or disease states. A cell’s behavior isn’t reducible to one gene, a network’s security isn’t dictated by a single node but by the interplay of all elements.

In cybersecurity, system-level resilience or vulnerability emerges from component interactions (e.g., servers, protocols). Cyber attacks often exhibit emergent behavior. Individual actions (e.g. phishing email, a malware infection) may seem trivial, but when combined across a network can cascade failures or sophisticated breaches. Data signatures with unique patterns identifying entities or behaviors emerge from aggregating and analyzing vast datasets. In cybersecurity, anomaly detection systems identify threats by recognizing emergent deviations from normal data patterns, a process to how biological systems detect foreign invaders.

This complexity also makes it vulnerable to destabilization. Emergent theory explains how small perturbations in complex systems like cyber attacks, economic shocks, or misinformation (Deepfake, AI, Human actors) can amplify into destabilizing forces. In cybersecurity, a minor exploit can trigger a systemic collapse; in economies, a rumor can crash markets. Network is the proliferation of how emergence expands through a system and is entwined.

Emergence and network topology are linked because the structure of a network are centralized or decentralized, or something in between fundamentally can shape the kinds of emergent behaviors and properties that arise. Centralized networks can lead to emergent fragility through failure of a critical component which impacts the system. Centralized control can produce emergent order quickly but creates an emergent vulnerability if the hub is compromised. In a monopoly, pricing or innovation may converge around the central player’s priorities. Centralization maintains control to concentrate power but also risk overload, leading to emergent inefficiencies or crises. Decentralized nodes connect peer-to-peer without a dominant hub can adapt to disruptions, an emergent property of redundancy.

Emergent behaviors vary as nodes operate independently. Emergent unpredictable outcomes can aggregate into system-wide patterns as self-organization occur as organic patterns without a central node to produce emergent stability or instability. Decentralized topologies foster emergent resilience and novelty, while centralized ones prioritize stability as it reaches the breaking point. Both can hit nonlinear thresholds. Centralized failures spread fast, while decentralized shifts gradually.

When Adam Smith talked about the invisible hand theory that drove the free market, was it a terminology for emergence? Markets are networks of individuals, firms linked by trade, investment, or information exchange which behave as network interactions from data variations produced representing the data activities. Network is the structure from which emergence is the description of the outcomes from context. In data, a sparse network might limit emergent insights, while a high nodes (e.g., social media) amplifies them.

Economics is emergent in behavior as market trends, bubbles, or crashes emerge from individual decisions aggregating into systemic phenomena. Prices emerge from supply and demand interactions, not as a single authority. Booms and busts (e.g., the 2008 financial crisis) emerge from feedback loops as confidence fuels investment, panic triggers sell-offs amplify small shifts into systemic events. Small inputs into a system result in new industries emerge from technological, social, and economic convergences.

In a monopoly, a few dominant nodes control flows of capital, goods, or information. Decisions are streamlined through centralized decisions. Through centralization, vulnerability can occur from the failure of a key node (e.g., Lehman Brothers, 2008) that can trigger emergent cascades, collapsing the system. Decentralized topology in economics are many nodes that connect peer-to-peer, like farmers’ markets or blockchain-based finance. Since the network structure is peer-to-peer no single failure sinks the system. Diverse network nodes allow niche markets and innovations emerge organically but can be susceptible to volatility through a lack of coordination.

Data systems are inherently networked through the internet, social media graphs, or database architectures. Emergent properties arise when raw data points interact to reveal pattern. Nodes (e.g., users, servers, consumers) and edges (e.g., hyperlinks, transactions) form structures that dictate how data flows, accumulate into actionable insights. Network effects amplify data’s economic value as an emergent property of scale and connectivity, and it seems difficult to imagine data as a silo usage since it’s always used as a comparison to something else.

One is the loneliest number that you'll ever do — Richard Patrick

The data economy thrives on emergent market trends, consumer behaviors or value that arise from billions of individual data points interacting. Which brings us to recent discussions on historical data and synthetic data being used to train AI models. Historical data can be compared with WAV data, it is the representation of a snapshot in a period that is emergent and differentiated. It is documentation of historical, behavioral occurrence and emerging markets by sequence of events.

Synthetic data is like MIDI data as both can be easily modified, recombined, and repurposed. Synthetic data is an impression of realism to mimic the real world, or in the case of MIDI, real instruments. Synthetic data is the representation of the trained historical data. Since historical data is in short supply as this point at the advent of AI, synthetic data has been able to meet the requirements of AI models while maintaining privacy-compliant and anonymity of data. Synthetic data like MIDI can generate many different outputs from the same core instructions regardless of context.

Free will through emergence is complicated. An individual make choices based on affordance and resource availability through constraints and opportunities of complex systems (e.g., social norms, economic conditions) to shape what’s possible. Free will operates within an emergent context, not in a vacuum. A democratic nation should ideally establish policies for its citizens to not just contribute to the social cultural intellectual knowledge capita of a nation but develop economical systems in which individuals can fulfill growth trajectory that trickle down to its system to produce a certain economical expression that represent historical data and informational representation.

Emergent consensus requires time as individual pattern and order arise from public opinion shifts, protests, or elections for democratic systems evolve through emergent feedback.

Democratic systems lets them unfold, aligning with complex adaptive systems. There is wisdom in the crowd which is an emergent property from diverse perspectives yet the irony is the mob that can destabilize democracy. Emergence is an observation, it can be observed in negative. Powerful actors through centralization can undermine the ideal emergence agency. Authoritarian structure rely on positive feedback through control to avoid bottom-up spontaneous, adaptive properties that define emergent systems.

Emergence is compatible with democracy’s decentralized ethos and individualism, of openness to adaptation and divergence but requires continuous refinement through feedback loops to account for emergent factor to establish equilibrium through active management to preserve democratic integrity.

Scientific rationalism fought against the tyranny of the church which was the state, yet tyranny can be any organizational structure even science itself. Given my training in life science, I would opt for democratic systems that self improves through feedback loops with enabled human freedom. I hope you enjoyed reading this article I wrote for you.

References

Hashemi-Pour, C., Yasar, K., & Laskowski, N. (2024, December 26). What is synthetic data? examples, use cases and benefits: TechTarget. Search CIO. https://www.techtarget.com/searchcio/definition/synthetic-data

Ibm. (2025, February 26). What is synthetic data?. IBM. http://ibm.com/think/topics/synthetic-data

Johnson, S. (n.d.). A Review of Emergence: The Connected Lives of Ants, Brains, Cities and Software. Review: Emergence by Steven Johnson. https://currents.dwrl.utexas.edu/fall04/leslie.html

Kavenna, J. (2019, October 4). Shoshana Zuboff: “surveillance capitalism is an assault on human autonomy.” The Guardian. https://www.theguardian.com/books/2019/oct/04/shoshana-zuboff-surveillance-capitalism-assault-human-automomy-digital-privacy