Author Archives: Kuncoro Wastuwibowo

The US Trade Imbalance

The United States has long been concerned about its persistent trade imbalance, frequently attributing responsibility to its business-partner countries for the gap between imports and exports. However, it should be recognised that most of the imbalance originates internally, driven by American corporations’ strategic pursuit of short-term profits, often through aggressive offshore profit-shifting practices. American businesses with the highest capitalisation, such as Apple, Google, and Microsoft, significantly contribute to this imbalance by establishing subsidiaries in low-tax jurisdictions like Ireland or Bermuda, legally diverting profits and depriving the US Treasury of critical tax revenues.

Apple has routinely utilised offshore structures, holding over $200 billion overseas at one point, strategically positioning intellectual property (IP) subsidiaries in countries with more favourable tax policies. Similarly, Google’s “Double Irish with a Dutch Sandwich” facilitated the shifting of billions in global advertising revenue, resulting in minimal domestic taxation. These practices are typically legal yet ethically contentious, with annual corporate profit-shifting estimated between $300 and $350 billion, leading to approximately $100–$150 billion in lost US tax revenue each year, according to estimates from the Congressional Budget Office and economist Gabriel Zucman.

In addition to technology firms, professional service companies such as McKinsey, other consultancy firms, and numerous US law firms frequently establish regional offices overseas, ensuring substantial earnings remain offshore. Although these practices are mostly legal, they highlight the significant internal roots of the trade imbalance, reflecting structural issues in corporate governance and tax policy rather than external economic aggression.

To meaningfully address these challenges, the US should initiate comprehensive internal reforms, beginning with corporate governance. A decisive shift from shareholder capitalism—prioritising quarterly profits—to stakeholder capitalism, where companies equally value long-term investments in employees, communities, and sustainability, is essential. The 2019 Business Roundtable statement was a symbolic step in this direction, but substantial action has been limited. True reform necessitates redefining executive compensation to incentivise sustainable, long-term growth rather than stock price manipulation through buybacks.

On the policy front, the US government should strengthen anti-profit-shifting measures by enhancing transparency through mandatory country-by-country financial reporting and enforcing stringent economic substance requirements. Implementing the OECD-backed global minimum tax (15%) could curb excessive offshore tax arbitrage by ensuring multinationals pay fair taxes irrespective of where they report profits. Additionally, penalising superficial offshore structures while incentivising genuine domestic investments could significantly mitigate revenue losses.

Ethically, American corporate culture should evolve to reject aggressive tax avoidance as standard practice. Promoting ethical standards and responsible business conduct, supported by public advocacy, investor pressure through Environmental, Social, and Governance (ESG) criteria, and transparent financial disclosures, could substantially reshape corporate behaviour. Institutional investors, pension funds, and even individual consumers can wield considerable influence by rewarding ethical corporate actions and penalising short-termist, exploitative strategies.

Ultimately, resolving the US trade imbalance is not solely about external tariffs or punitive measures against other nations but requires confronting internal structural issues directly. By embracing rigorous regulatory reforms, incentivising ethical corporate governance, and committing to strategic long-term economic planning, America can effectively rebalance trade, recover significant lost revenues, and foster sustainable economic prosperity for future generations.

The Flawed Global Ecosystem Strategy

Last century, the US stood as the pinnacle of industrial power. With unmatched manufacturing capacity, cutting-edge innovation, and a dynamic domestic labour force, the US not only produced at scale, but also created a vast middle class through industrial employment. But since the early 21st century, this dominance had eroded. Despite the continued global success of Apple, Microsoft, etc, the US found its industrial core hollowed out. This paradox—where the strategy won, but the nation did not—is at the heart of this exploration.

The US led the global shift toward liberalisation and globalisation, embracing free trade, deregulation, and offshoring as strategies for economic growth and competitive advantage. These ideas crystallised during the Reagan-Thatcher era and were institutionalised in policies such as NAFTA and the support for China’s entry into the WTO. The logic was simple: relocate labor-intensive manufacturing to lower-cost countries, focus domestically on high-value services and innovation, and reap the benefits of global efficiency.

For US corps, this approach worked magnificently. Apple built one of the most valuable ecosystems in the world, with tightly integrated design, software, services, and hardware. But much of this hardware was manufactured and assembled overseas, particularly in China. Microsoft dominated software and enterprise services, but its global cloud and platform ecosystem increasingly depended on international data centers, developer networks, and supply chains that were vulnerable to political shifts.

What became apparent over time was that these ecosystem-based strategies—while brilliant in achieving scale, market control, and profitability—were fundamentally fragile. They were built on assumptions of a stable global environment, unrestricted cross-border flows of labour, capital, and data, and a geopolitical consensus that no longer exists. The COVID-19 pandemic, the US-China business war, and the rise of protectionist and nationalist policies globally exposed just how brittle these supply chains and platform dependencies were.

The heart of the flaw is in the over-optimisation for efficiency at the expense of resilience. By offshoring critical manufacturing, the US lost not only jobs but also industrial knowledge, logistics infrastructure, and the ability to rapidly pivot production domestically in times of crisis. This strategic vulnerability became clear when shortages of semiconductors, PPE, and other essentials during the pandemic brought entire industries to a standstill.

Moreover, the US model of capitalism encouraged short-termism. Public companies were driven to maximise quarterly earnings and shareholder returns, often by cutting labor costs or outsourcing rather than reinvesting in domestic capacity. Labor unions weakened significantly, and with them, the political and social infrastructure that once supported a strong working class. The cultural shift toward a “knowledge economy” reinforced the idea that physical production was less valuable than digital platforms, intellectual property, and financial engineering.

This ideology extended into the UK as well, which closely mirrored US strategies in economic liberalization. Under Thatcher in the 1980s, the UK privatized major industries, deregulated finance, crushed unions, and repositioned itself as a global hub for services—especially financial services. The “Big Bang” of 1986 opened up London’s financial markets, turning the City into a magnet for global capital. Much like the US, the U.K. allowed its manufacturing base to atrophy in favour of high-value services concentrated in the Southeast, particularly London.

However, the UK, unlike the US, lacked the scale, resource diversity, and global technological dominance to buffer the negative effects of this transition. The result was stark regional inequality, declining productivity, and chronic underinvestment in infrastructure and education in much of the country. Brexit, in many ways, was the political expression of this economic alienation—a rebellion against globalisation, centralisation, and the perception of being “left behind.”

In both countries, we see a core contradiction: while companies triumphed globally, the broader national economies suffered from fragility, inequality, and a loss of sovereignty in key strategic sectors. The ecosystem-based strategies of firms like Apple and Microsoft continue to generate massive returns, but they do so by depending on fragile geopolitical arrangements, low-cost labor overseas, and complex, just-in-time logistics networks that are increasingly prone to disruption.

The irony is that ecosystems, as conceptualised in nature, thrive on diversity, redundancy, and mutual support. Business ecosystems, as built by the tech giants, often lack these qualities. They tend toward centralisation, dominance, and efficiency, making them look more like monocultures than true ecosystems. When stress hits—in the form of sanctions, pandemics, or trade wars—these systems do not bend; they break.

So is the ecosystem model flawed? Not entirely. It remains one of the most powerful frameworks for value creation in a networked economy. But it needs to evolve. Firms must build ecosystems that are not just efficient, but resilient and adaptable. This means diversifying supply chains, investing in local capabilities, supporting the long-term health of partners, and accounting for political and environmental risks.

Nations, too, must rethink their approach. A return to protectionism is not the answer, but neither is blind faith in market liberalism. Strategic sectors must be rebuilt or supported domestically not only for economic competitiveness but for national resilience. Policies must incentivise long-term investment, regional regeneration, and industrial policy aligned with innovation.

Ultimately, the story of the past few decades is not that globalization and liberalization were inherently wrong. Rather, they were applied too narrowly, with too little foresight, and with insufficient regard for the long-term health of national economies. The US and the UK offer lessons—both cautionary and hopeful—for any country navigating the next era of global business, where resilience, sovereignty, and inclusive prosperity will be just as important as efficiency and innovation.

Information at the Heart of Complexity

In The Complex World, a book written by David Krakauer as an intro to the foundations of Complexity Theory, a striking passage declares in the Chapter on Information, Computation, and Cognition: “information and information processing lie at the heart of the sciences of complexity.” This powerful statement not only encapsulates the essence of complexity science but also invite to explore how foundational ideas from information theory and historical philosophy have reshaped our understanding of the intricate systems that govern nature, technology, and society.

At the forefront of this intellectual revolution stands Claude Shannon, whose seminal 1948 work laid the groundwork for modern information theory. Shannon introduced the concept of quantifying information through measures such as entropy and redundancy, offering a robust mathematical framework to analyse how messages are encoded, transmitted, and decoded. His groundbreaking insights transformed the way we understand communication and paved the way for examining complex systems through the lens of information exchange.

Claude Shannon

Building on Shannon’s legacy, early pioneers like Norbert Wiener in cybernetics explored how feedback loops and control mechanisms underpin both living organisms and machines. These studies revealed that all systems — whether biological, electronic, or social — operate through continuous cycles of processing and exchanging information. This realisation led to a shift in perspective: rather than viewing components in isolation, researchers began to see the dynamic interactions and feedback as the true drivers of emergent behavior.

Central to complexity science is surely the idea that complex systems are composed of numerous interacting parts whose collective behavior gives rise to phenomena that are not apparent from the properties of individual components. The complexity of information itself reflects the system’s potential for emergence. As information becomes more intricate, its diverse possibilities create the fertile ground for spontaneous order and structure to arise. In this sense, the complexity embedded within information mirrors the layered reality it represents.

Analytically, viewing systems as networks of information processors has led to the development of powerful computational models. Cellular automata, agent-based simulations, and network analyses allow scientists to investigate how simple local rules of interaction can culminate in sophisticated global patterns. These models quantify the flow of information and reveal that small changes in how data is processed can lead to dramatic shifts in system behavior—underscoring the role of information in driving emergent phenomena.

Furthermore, this perspective is enriched by concepts such as Holland’s signals and boundaries, which describe how interactions at the edges of systems give rise to organised patterns. Signals act as the carriers of information across boundaries, defining the interfaces where local interactions take place. These interactions are critical in establishing the rules by which complex behaviors emerge, demonstrating that even at the micro-level, the quality and complexity of information can have far-reaching implications on the overall structure and dynamics of a system.

Ultimately, the convergence of Shannon’s revolutionary insights, the pioneering work in cybernetics, and the evolution of systems theory all lead us to the compelling conclusion mentioned above: information and information processing lie at the heart of the sciences of complexity. This understanding not only provides a unifying framework across disciplines but also highlights how the inherent complexity of information — measured in its entropy and intricate signals —mirrors and shapes the emergent realities of our world.

Signals and Boundaries

John Holland’s “Signals and Boundaries” has become a touchstone in the study of complex adaptive systems (CAS), offering an intuitive way to understand how local interactions give rise to emergent behavior. At its core, Holland’s framework posits that signals (=the carriers of information) and boundaries (=the limits that define and protect modules) play a pivotal role in the organisation, adaptation, and evolution of complex systems. His insights have helped shape our understanding of how simple, localised exchanges can lead to intricate global patterns.

The framework’s influence is widespread, resonating strongly within academic circles including the SFI. Scholars have incorporated Holland’s ideas into broader discussions on network theory and modularity, using them as a bridge between traditional adaptation models and more modern computational approaches. By emphasising the dual roles of communication through signals and compartmentalisation via boundaries, Holland provided researchers with a practical toolkit for analysing the dynamics of ecosystems, technological platforms, and social networks.

Holland’s Signals and Boundaries, read at the Soekarno Hatta International Airport

A significant strength of Holland’s theory lies in its capacity to illustrate how local interactions can generate emergent complexity. When agents within a system interact, they exchange signals that serve as feedback loops—adjusting behavior and influencing neighboring agents. Meanwhile, boundaries help maintain structure by isolating specific interactions from external noise, allowing subsystems to develop independently yet remain interconnected. This delicate balance between isolation and connectivity is what drives the self-organisation and adaptation observed in complex systems.

However, the notion that complexity is solely the product of local interactions has its critics. Some argue that focusing exclusively on bottom-up processes might neglect the role of global influences and top-down causation. In many systems, overarching constraints, environmental factors, and collective dynamics impose patterns and behaviors that local interactions alone cannot fully explain. This perspective contends that emergent phenomena may also be shaped by these global forces, suggesting a need for models that integrate both micro-level interactions and macro-level structures.

One contrasting perspective within the complexity paradigms is the idea of strong emergence. Proponents of strong emergence assert that certain higher-level properties of a system are fundamentally irreducible to the interactions of its constituent parts. In this view, while local interactions are essential, they cannot entirely account for phenomena that manifest at the macro scale. The emergent behaviors observed in complex systems may require explanations that go beyond the sum of local interactions, implying that there are holistic properties at play that necessitate a different conceptual approach.

There is also a growing consensus among some researchers that a dual approach—one that synthesises both local and global perspectives—is necessary for a complete understanding of complexity. Network theorists and systems dynamicists, for example, have highlighted the importance of long-range correlations and global feedback loops that complement local interactions. This integrated approach recognises that while signals and boundaries are crucial, the interplay with broader systemic forces can drive self-organisation and adaptation in ways that are not captured by local dynamics alone.

Holland’s signals and boundaries framework remains a seminal contribution to complexity science, celebrated for its clarity and applicability across diverse domains. It has provided a powerful lens for examining how decentralised, local interactions can lead to emergent behavior—a notion that has profoundly influenced our understanding of ecosystems, technological platforms, and social networks. Yet, as our grasp of complex systems deepens, it is equally important to acknowledge and incorporate contrasting views, such as the roles of strong emergence and global influences, to capture the full richness of complexity. This ongoing dialogue not only enriches the theoretical landscape but also drives innovation in how we model and manage complex systems in practice.

Cities Development as CAS

The research titled “Inter-City Firm Connections and the Scaling of Urban Economic Indicators” by Yang, Jackson, and Kempes, published in PNAS Nexus (Nov 2024), presents a fresh perspective on how cities generate economic output. While traditional urban scaling theories focus on how local, intra-city interactions drive economic productivity, this study argues that inter-city connections — especially through multinational firms — play an equally, if not more, significant role. By analysing GDP data from cities in the US, EU, and PRC, alongside the Global Network Connectivity (GNC) of multinational firms, the study reveals that cities with higher inter-city connectivity exhibit higher-than-expected GDP, even after accounting for population size. This finding challenges the conventional idea that urban scaling is driven solely by local social interactions, offering a new lens for understanding complexity in urban systems.

This study is an example of how complexity science can be applied to real-world systems like cities. Cities, as complex adaptive systems (CAS), exhibit emergent behaviours, such as superlinear scaling of GDP, where larger cities tend to be disproportionately more productive. Traditionally, this emergent property was attributed to denser local social interactions. However, the authors introduce a new dimension of complexity by demonstrating how inter-city firm connections serve as an additional mechanism for economic emergence. Using the concept of networked systems, cities are modelled as nodes connected by firms, and the GNC score quantifies the strength of these connections. The research shows that GDP is influenced not just by a city’s local population but also by its position within this global network. This insight extends the complexity science framework by highlighting the role of cross-city organisational linkages in shaping global economic output.

The study also provides methodological advances that enrich the complexity science toolkit. It uses Scale-Adjusted Metropolitan Indicators (SAMI) to compare how cities “overperform” or “underperform” in GDP relative to expectations. This allows for a nuanced view of which cities benefit most from inter-city connections. Furthermore, the use of multilevel regression models that incorporate both local (population) and global (GNC) factors reveals the nonlinear dynamics at play. Such nonlinear scaling, where population alone cannot explain GDP growth, suggests the presence of feedback loops where better-connected cities become more prosperous, and prosperous cities become better connected. These insights underscore how complexity science can offer more accurate, multi-layered models of urban growth, moving beyond simplistic population-based approaches.

The implications of this research go beyond academic curiosity. For policymakers, it suggests that urban economic development strategies should prioritise enhancing global connectivity. Cities can benefit from strengthening ties with multinational firms, facilitating cross-city collaborations, and becoming key nodes in the global urban network. This is a shift from the classic focus on improving only local conditions, such as infrastructure or intra-city mobility. For complexity science, this study exemplifies how theories of self-organisation, emergence, and adaptive networks can be operationalised in practical, high-impact research. The work highlights the potential for developing a more comprehensive urban scaling model that integrates both local and global processes. By bridging concepts from complexity science with urban development, the study opens new possibilities for future research into how global interconnections influence local outcomes, from economic growth to social inequality.

Source: Vicky Chuqiao Yang, Jacob J Jackson, Christopher P Kempes, 2024, Inter-city firm connections and the scaling of urban economic indicators, PNAS Nexus 3:11, DOI: 10.1093/pnasnexus/pgae503

Tema Awal 2025

Annual letter dari Future Today Institute memaparkan situasi yang terjadi di akhir 2024 serta dampak yang perlu dipertimbangkan di 2025. Ringkasannya dipaparkan di bawah ini.

1. Technology Supercycle: Tahun 2025 menandai dimulainya “Supercycle Teknologi” yang dipicu oleh konvergensi teknologi-teknologi baru seperti AI, sensor canggih, dan bioteknologi. Periode percepatan inovasi ini dapat menyaingi revolusi besar sebelumnya seperti listrik dan internet, memicu pergeseran ekonomi, munculnya industri baru, dan transformasi sosial.

2. Living Intelligence: Lebih dari sekadar AI, sistem “kecerdasan hidup” akan menggabungkan AI, sensor canggih, dan bioteknologi untuk menciptakan sistem yang dapat beradaptasi dan belajar sendiri. Sistem ini akan mengubah industri dan pasar, mendorong para pemimpin untuk melampaui pemikiran berbasis AI semata agar dapat menangkap peluang dari konvergensi ini.

3. PLAMs, CLAMs, & GLAMs: Evolusi dari LLM (Large Language Models) ke LAM (Large Action Models) akan memungkinkan eksekusi tugas secara real-time, bukan hanya pembuatan konten. Model tindakan pribadi (PLAM), perusahaan (CLAM), dan pemerintahan (GLAM) akan mengotomatiskan pengambilan keputusan, merampingkan pengalaman pengguna, dan beroperasi secara mandiri dengan memanfaatkan data perilaku.

4. Weird Tech Alliances: Kemitraan yang tak terduga, seperti Apple yang menggunakan chip pelatihan AI milik Amazon, menandakan pergeseran menuju kolaborasi lintas industri. Para pemain besar cloud seperti AWS, Microsoft, dan Google semakin banyak bermitra dengan raksasa teknologi lainnya untuk mengembangkan infrastruktur AI generasi berikutnya.

5. Crypto Winter Thaws: Kenaikan Bitcoin hingga mencapai $100K terkait dengan terpilihnya Donald Trump, yang berjanji menjadikan AS sebagai “pusat kripto dunia” dengan mendorong deregulasi pasar. Usulan Trump untuk menciptakan cadangan strategis kripto dan pengangkatan tokoh pro-kripto sebagai ketua SEC mengisyaratkan kondisi yang lebih menguntungkan bagi pertumbuhan mata uang kripto pada tahun 2025.

6. Quantum Computing’s Breakthrough: Kemajuan dalam koreksi kesalahan dan sistem hybrid kuantum-klasik mendorong komputasi kuantum ke arah komersialisasi. Investasi dari Google, IBM, dan pemerintah AS bertujuan membuat sistem kuantum lebih mudah diakses, dengan sistem hybrid menjadi peluang bisnis dalam waktu dekat.

7. Climate Tech Innovation: Perubahan iklim akan meningkatkan permintaan terhadap inovasi teknologi seperti desalinasi, beton pengurang karbon, dan alternatif GPS. Seiring meningkatnya cuaca ekstrem, kebutuhan akan infrastruktur yang tangguh akan mendorong percepatan komersialisasi dan adopsi teknologi iklim.

8. Nuclear Energy Comeback: Reaktor Modular Kecil (SMR) semakin diminati sebagai alternatif bersih dan skalabel untuk pembangkit listrik tenaga nuklir tradisional. Microsoft, Google, dan Amazon berinvestasi dalam SMR untuk memasok energi pusat data mereka. Pemerintah AS juga mendukung pengembangan SMR, dan energi fusi mungkin akan mengalami terobosan besar pada tahun 2025.

9. Chaos in Europe: Ketidakstabilan politik di Prancis dan Jerman akan melemahkan kemampuan Eropa dalam mendorong inovasi, terutama dengan diberlakukannya UU AI Uni Eropa pada tahun 2025. Tanpa kepemimpinan yang kuat, sektor “Mittelstand” Jerman dan ekosistem teknologi Prancis mungkin kesulitan, yang pada akhirnya dapat mengurangi daya saing Eropa secara keseluruhan.

10. Washington’s Game of Thrones: Para miliarder teknologi, yang diperkaya oleh pemerintahan Trump, akan semakin menguasai proses pembuatan kebijakan di AS. Pengaruh Lembah Silikon di Washington akan meningkat, menggantikan otoritas tradisional pemerintah, karena para pemimpin teknologi memanfaatkan kekayaan dan pengaruh mereka untuk membentuk kebijakan yang menguntungkan mereka.

Sumber:
Webb, Amy. Annual Letter — 2025 Macro Themes + 2024 Signals Review. Future Today Institute. [URL]

6G Network

The 6G network will be the next big step in mobile technology, expected to launch around 2030. Currently in the research phase, it promises to go far beyond 5G and 4G with faster speeds, lower latency, greater capacity, and better connectivity. Using THz frequencies for higher bandwidth, AI for smarter networks, and quantum communication for advanced security, 6G will power exciting applications like holographic communication, brain-machine interfaces, autonomous systems, and the Internet of Everything (IoE), paving the way for a highly connected and intelligent future.

The foundational advancement of 6G indicates significant performance enhancements over previous generations:

  1. Spectrum Efficiency: With 5–10x improvement over 5G, 6G will maximise the spectrum use, enabling high-capacity transmissions for increasing network demands.
  2. Peak Data Rates: Exceeding 1 Tb/s, 6G will support next-generation applications like holographic communications and high-resolution immersive experiences.
  3. Latency: Reduced to 10–100 µs for over-the-air (OTA) transmissions, 6G enables ultra-reliable real-time applications such as brain-machine interfaces, autonomous systems, and tactile internet.
  4. Mobility: With support for 1000 km/h speed, 6G supports high-speed transportation systems like hypersonic travel and advanced railway systems.
  5. Connectivity Density: Connecting >10⁷ devices/km² will support dense IoT ecosystems, including smart cities, industrial automation, and ambient intelligence.
  6. Energy Efficiency: Efficiency to be improved 100 times, emphasising sustainability and minimising the environmental impact of the growing digital ecosystem.
  7. Traffic Capacity: With an area traffic capacity of up to 1 Gbps/m², 6G will provide consistent performance in densely populated urban centres and during high-traffic events.

6G technology is designed to address diverse and futuristic use cases, grouped into key verticals:

  1. Enhanced eMBB (FeMBB)
    • Holographic Verticals: Real-time holographic telepresence for virtual meetings, education, and entertainment.
    • Full-Sensory Digital Sensing and Reality: Immersive experiences that incorporate multiple senses in digital interactions.
    • UHD/SHD/EHD Videos: Ultra-high-definition video streaming for cinematic-quality remote collaborations.
    • Tactile/Haptic Internet: Real-time transmission of touch and feedback for applications like telemedicine and virtual reality.
  2. Enhanced Ultra-Reliable Low-Latency Communications (ERLLC)
    • Fully Automated Driving: Safe and reliable real-time communication for autonomous vehicles in urban and highway settings.
    • Industrial Internet: High-precision and responsive connectivity for smart factories, robotics, and industrial IoT systems.
  3. Massive Machine-Type Communications (umMTC)
    • The Internet of Everything (IoE) will become a reality with comprehensive integration of devices, systems, and environments, driving smart cities and personalised services.
  4. Enhanced Low Power Communications (ELPC)
    • Internet of Bio-Nano-Things: Advanced nanoscale connectivity for healthcare and biological systems.
  5. Long-Distance High-Mobility Communications (LDHMC)
    • Space Travel: Reliable communication for interplanetary exploration and space tourism.
    • Deep-Sea Sightseeing: Advanced communication systems for underwater exploration and operations.
    • Hyperspeed Railways: Seamless connectivity for passengers traveling at speeds greater than 1000 km/h.
  6. Energy Efficiency and Environmental Goals
    • Energy Harvesting: Devices will capture energy from ambient sources such as solar power or electromagnetic waves, reducing dependence on batteries.
    • Zero-Power Communications: Some devices will operate solely on harvested energy, making them ideal for IoT in remote or inaccessible locations.
    • AI-Driven Energy Management: Artificial intelligence will optimize resource allocation across the network, ensuring minimal power usage without compromising performance.

Complexity Science for AI?

AI technologies are primarily developed by advancements in machine learning, particularly deep learning and natural language processing. An example is ChatGPT, which is built on the Transformer architecture, and employs deep neural networks with attention mechanisms to process and generate human-like text. While the architecture of these models is inherently complex, characterised by vast parameters and intricate layers, they do not rely heavily on Complexity Science as a core framework in their design or functionality.

There are actually some indirect connections between AI and Complexity Science. Deep neural networks, for instance, can be conceptualised as complex systems where simple components (neurons) interact to produce emergent behaviours, such as understanding and generating language. While Complexity Science provides valuable insights into such emergent phenomena, these principles are not the primary foundation for AI model development. Complexity Science concepts are also applied in training optimisation, where researchers study high-dimensional optimisation landscapes, convergence properties, and loss surface dynamics to improve the stability and efficiency of training processes. Interpretability in AI benefits from complexity-based approaches like network theory and information theory, which help uncover how information flows through neural networks. Another area of overlap is robustness and generalisation, where ideas from Complexity Science and statistical mechanics, such as phase transitions and criticality, aid in understanding why large, over-parameterised models perform well in real-world scenarios.

Despite these connections, we must acknowledge that Complexity Science has not been a major driving force in the development of AI technologies like ChatGPT. The creation of such models relies more on advances in neural network architectures, data processing, and algorithmic optimisation than on the theoretical foundations of Complexity Science.

There are some opportunities if we can enrich AI with principles from Complexity Science. It could enhance the adaptability, robustness, and interpretability of AI systems by providing them a better ways of managing dynamic, non-linear interactions and uncertainty in real-world environments. This integration could enable the creation of AI models that handle emergent behaviours more effectively, excel in predictive analytics, and exhibit greater resilience, moving the field closer to achieving general, human-like intelligence.

The challenges are on behalf of Complexity Science. One major limitation is its lack of standardised, predictive methods that can be broadly applied to complex systems. Most Complexity Science models are descriptive or exploratory, emphasising qualitative understanding over quantitative prediction. Additionally, complex systems are often highly context-dependent, making it difficult to generalise findings or develop uniform approaches. Computational intensity is another barrier; many complexity-based models, such as agent-based simulations, struggle to scale to the large datasets typical in AI. Furthermore, Complexity Science has historically focused on theoretical and simulation-based methods, while AI thrives on data-driven approaches, creating a methodological gap. Finally, the absence of a unified theoretical framework in Complexity Science makes it still challenging to translate its principles into practical, standardised tools for AI.

Complexity Science offers profound insights into the behaviour of complex systems but remains underdeveloped in areas critical for its integration with AI, such as predictive capability, scalability, and standardisation. As interdisciplinary research progresses and computational capabilities grow, these limitations may be addressed, unlocking new opportunities for AI systems to benefit from the rich, nuanced perspectives of Complexity Science.

Teori Institusi

Hadiah Nobel Ekonomi dianugerahkan tahun 2024 ini pada Daron Acemoglu, Simon Johnson, dan James A. Robinson, sebagai pengakuan atas Teori Institusi yang mereka kembangkan. Anugerah ini diumumkan 9 Oktober 2024, dengan tambahan bahwa teori mereka memberikan wawasan tentang penyebab kemiskinan atau kekayaan berbagai negara, lengkap dengan panduan bagi kebijakan pembangunan dan reformasi institusi.

Teori Institusi mengungkapkan bahwa kemakmuran suatu negara bukan sekadar ditentukan oleh faktor geografis, budaya, atau sumber daya alam; namun lebih oleh institusi, yang dalam hal ini berarti aturan, kebijakan, dan struktur sosial. Institusi ini memainkan peran kunci dalam mendorong atau menghambat kemajuan ekonomi. Paran pengembang teori ini membagi institusi atas institusi inklusif dan institusi ekstraktif.

Institusi inklusif adalah institusi yang memungkinkan partisipasi luas dari masyarakat dalam kegiatan ekonomi. Dengan adanya perlindungan terhadap hak kepemilikan, jaminan kesetaraan peluang, dan dorongan terhadap inovasi, institusi inklusif memungkinkan banyak orang untuk ikut serta dalam pembangunan ekonomi. Sebaliknya, institusi ekstraktif berfungsi dengan cara yang bertolak belakang. Kekuasaan dan kekayaan terkonsentrasi di tangan sekelompok kecil elit. Akibatnya, sebagian besar masyarakat terpinggirkan dari akses ekonomi, dan inovasi menjadi terhambat. Negara-negara dengan institusi ekstraktif cenderung terperangkap dalam lingkaran kemiskinan dan ketidaksetaraan.

Salah satu elemen menarik dari teori ini adalah konsep critical junctures atau persimpangan kritis. Ini adalah momen-momen penting dalam sejarah suatu bangsa—seperti revolusi, perang, atau penjajahan—yang bisa mengubah arah jalur institusional mereka. Pada saat-saat inilah masyarakat bisa memilih untuk membangun institusi yang lebih inklusif atau malah memperkuat institusi yang ekstraktif. Contoh klasik yang sering diangkat adalah perbedaan nasib antara Amerika Utara dan Amerika Latin setelah kedatangan penjajah Eropa. Amerika Utara, dengan iklim dan kondisi lingkungan yang cocok untuk pemukiman, cenderung mengembangkan institusi yang melibatkan masyarakat secara luas. Sebaliknya, Amerika Latin, dengan sumber daya alam yang berlimpah, justru menarik para penjajah untuk membangun sistem berbasis eksploitasi sumber daya. Dampaknya, Amerika Utara berkembang menjadi wilayah yang lebih makmur dan stabil secara politik, sementara Amerika Latin terus bergulat dengan ketimpangan sosial dan ekonomi.

Hal lain yang tidak kalah penting adalah konsep sentralisasi kekuasaan politik. Institusi yang baik butuh dukungan dari kekuasaan politik yang kuat dan terpusat. Mengapa? Karena tanpa kekuasaan terpusat, aturan hukum sulit ditegakkan, dan konflik kepentingan menjadi lebih sering terjadi. Namun, sentralisasi ini harus disertai dengan akuntabilitas. Tanpa akuntabilitas, kekuasaan politik yang kuat bisa berubah menjadi sistem yang opresif dan ekstraktif. Bayangkan negara-negara otoriter di mana penguasa mengontrol segalanya tanpa pengawasan—sistem semacam ini cenderung membangun institusi ekstraktif yang hanya menguntungkan segelintir orang.

Selain itu, ada fenomena yang disebut pergeseran institusi; yaitu perubahan kecil yang terjadi secara bertahap dalam jangka panjang. Pergeseran ini bisa memperkuat sistem inklusif atau, sebaliknya, justru membuat institusi yang tadinya inklusif menjadi ekstraktif. Misalnya, reformasi hukum kecil-kecilan atau perubahan kebijakan tertentu mungkin terlihat sepele, tapi jika dilakukan secara terus-menerus, dampaknya bisa besar dalam jangka panjang. Inilah mengapa dinamika kekuasaan politik sangat penting. Elit yang diuntungkan dari sistem ekstraktif cenderung akan menolak perubahan, karena mereka tidak ingin kehilangan akses ke kekuasaan dan kekayaan.

Pendekatan mereka juga didukung oleh banyak bukti empiris. Salah satu penelitian mereka yang paling terkenal adalah tentang warisan kolonial. Dalam penelitian tersebut, mereka menunjukkan bahwa wilayah-wilayah yang di masa lalu membangun institusi ekstraktif selama era kolonial, seperti kebun-kebun besar di Afrika atau Amerika Latin, saat ini masih mengalami masalah kemiskinan dan ketidaksetaraan yang tinggi. Sebaliknya, wilayah-wilayah yang membentuk institusi inklusif, seperti Amerika Utara, saat ini cenderung lebih stabil secara politik dan lebih makmur secara ekonomi. Peristiwa penting lain yang sering mereka soroti adalah Revolusi Agung (Glorious Revolution) di Inggris, di mana sistem monarki absolut diubah menjadi sistem monarki konstitusional yang lebih inklusif, sehingga memungkinkan lahirnya lembaga-lembaga ekonomi modern yang lebih terbuka dan partisipatif.

Lalu, bagaimana teori ini relevan untuk manajemen strategis? Dalam dunia bisnis, perusahaan tidak bisa lepas dari pengaruh institusi di negara tempat mereka beroperasi. Jika suatu negara memiliki institusi inklusif, maka bisnis memiliki peluang lebih besar untuk berkembang. Sebaliknya, di negara-negara dengan institusi ekstraktif, perusahaan sering menghadapi risiko yang lebih besar, seperti korupsi, ketidakpastian hukum, dan pengambilan keputusan yang didominasi oleh elit tertentu. Teori ini juga memberikan wawasan bagi perusahaan multinasional yang ingin melakukan ekspansi global. Sebelum berinvestasi di negara tertentu, penting untuk menganalisis apakah institusi di negara tersebut bersifat inklusif atau ekstraktif. Perusahaan dapat menggunakan pemahaman ini untuk memetakan risiko dan merancang strategi mitigasi yang lebih efektif.

Menariknya, perusahaan bukan hanya aktor pasif dalam ekosistem institusional. Dalam beberapa kasus, perusahaan besar justru dapat memengaruhi bentuk institusi di suatu negara. Perusahaan yang kuat secara finansial dapat melobi perubahan kebijakan atau memperkuat status quo. Kadang-kadang, perusahaan membantu memperkuat sistem ekstraktif dengan mendukung regulasi yang menguntungkan mereka, tetapi di sisi lain, perusahaan juga dapat mendorong reformasi yang lebih inklusif, misalnya dengan mengadvokasi transparansi dan keadilan dalam peraturan pasar.

Sumber:
Acemoglu D, Johnson S, Robinson J, 2004. Institutions as the Fundamental Cause of Long-Run Growth, NBER Working Paper Series, National Bureau of Economic Research. URL: http://www.nber.org/papers/w10481

Ekonomi Kompleksitas

Arthur WB (2021) menulis paper yang membandingkan ekonomi konvensional (neoklasik) dengan ekonomi kompleksitas.

Ekonomi neoklasik konvensional didasarkan pada beberapa asumsi inti:

  1. Rasionalitas sempurna: Diasumsikan bahwa agen-agen ekonomi memecahkan masalah yang terdefinisi dengan baik menggunakan logika rasional sempurna untuk mengoptimalkan perilaku mereka.
  2. Agen representatif: Biasanya diasumsikan bahwa agen-agen ini serupa satu sama lain — mereka bersifat “representatif” — dan dapat dikategorikan ke dalam satu, sedikit, atau sejumlah kecil tipe yang mewakili.
  3. Pengetahuan bersama: Diasumsikan bahwa semua agen memiliki pengetahuan yang sama tentang tipe agen lain, bahwa agen lain juga sepenuhnya rasional, dan mereka berbagi pengetahuan umum ini.
  4. Keseimbangan: Diasumsikan bahwa hasil agregat konsisten dengan perilaku agen, sehingga tidak ada insentif bagi agen untuk mengubah tindakan mereka.

Namun, dalam 120 tahun terakhir, ekonom seperti Thorstein Veblen, Joseph Schumpeter, Friedrich Hayek, dan Joan Robinson menentang kerangka keseimbangan ini dengan alasan masing-masing. Mereka berpendapat bahwa diperlukan pendekatan ekonomi yang berbeda.

Pada tahun 1987, Santa Fe Institute mengadakan konferensi yang mengundang sepuluh teoretisi ekonomi dan sepuluh teoretisi fisika untuk mengeksplorasi ekonomi sebagai sistem kompleks yang terus berkembang.

Ekonomi kompleksitas melihat ekonomi bukan sebagai sistem yang selalu dalam keadaan seimbang, tetapi sebagai sistem yang terus berubah. Keputusan yang diambil oleh para pelaku ekonomi (atau agen) tidak diasumsikan superrasional, dan masalah yang mereka hadapi tidak selalu terdefinisi dengan baik. Ekonomi tidak lagi dipandang sebagai “mesin yang bekerja sempurna,” melainkan sebagai “ekologi” yang selalu berubah — berisi kepercayaan, prinsip pengorganisasian, dan perilaku yang terus berkembang.

Ekonomi kompleksitas menganggap bahwa setiap pelaku ekonomi berbeda satu sama lain, memiliki informasi yang tidak sempurna tentang agen lain, dan terus mencoba memahami situasi yang mereka hadapi. Agen-agen ini mengeksplorasi, bereaksi, dan terus-menerus mengubah tindakan dan strategi mereka berdasarkan hasil yang mereka ciptakan bersama. Hasil akhirnya mungkin tidak dalam keadaan keseimbangan dan dapat menunjukkan pola serta fenomena baru yang tidak terlihat dalam analisis keseimbangan. Ekonomi menjadi sesuatu yang tidak tetap dan ada begitu saja, tetapi terus berkembang melalui kumpulan tindakan, strategi, dan keyakinan yang sedang berkembang. Ekonomi tidak lagi mekanistik, statis, abadi, dan sempurna, melainkan organik, hidup, selalu menciptakan dirinya sendiri, dan penuh dengan dinamika yang rumit.

Perbandingannya dipaparkan dalam tabel berikut:

Dalam sistem kompleks, tindakan yang diambil oleh seorang agen disalurkan melalui jaringan koneksi. Dalam ekonomi, jaringan ini dapat terbentuk melalui perdagangan, transmisi informasi, pengaruh sosial, atau aktivitas pinjam-meminjam. Ada beberapa aspek menarik dari jaringan ini:

  1. Struktur interaksi atau topologi jaringan memengaruhi stabilitas.
  2. Jaringan memungkinkan pasar untuk mengatur diri mereka sendiri.
  3. Risiko dapat ditransmisikan melalui jaringan, peristiwa dapat menyebar, dan struktur kekuasaan dapat terbentuk.

Topologi jaringan sangat penting untuk menentukan apakah konektivitas meningkatkan stabilitas atau justru sebaliknya. Kerapatan koneksi juga memainkan peran penting. Jika sebuah peristiwa terjadi di jaringan yang jarang terhubung, dampaknya akan segera berhenti karena tidak ada jalur untuk penyebaran lebih lanjut. Namun, di jaringan yang sangat terhubung, peristiwa tersebut akan menyebar luas dan terus meluas dalam waktu yang lama. Jika jaringan perlahan-lahan meningkatkan tingkat konektivitasnya, sistem akan berubah dari memiliki sedikit dampak (atau tanpa dampak) menjadi dampak besar, bahkan menghasilkan konsekuensi yang tidak berakhir. Hal ini dikenal sebagai perubahan fase, salah satu ciri khas dari ekonomi kompleksitas.

Ekonomi kompleksitas, dengan fokusnya pada dinamika jaringan dan evolusi sistem, menawarkan cara baru untuk memahami perilaku ekonomi di dunia yang penuh ketidakpastian dan perubahan yang cepat.

Referensi: