You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Q:

Side-effects of adding tcolorbox and enumerate inside wrapfigure

I am pleased to have your help from ![my questions][A lot of unexpected spaces between paragraph after inserting colorbox such as ![this answer][https://tex.stackexchange.com/a/219787/14409] or ![the other answer][https://tex.stackexchange.com/a/219569/14409]. However, there has been another problem which I guess it is the side effect of adding tcolorbox, enumerate, wrapfig together. Below is MWE.
\documentclass[a4paper,12pt]{article}
\usepackage[scaled]{helvet}
\renewcommand\familydefault{\sfdefault}
\usepackage[utf8]{inputenc}
\usepackage{pifont}
\usepackage{wrapfig}
\usepackage[framemethod=tikz]{mdframed}
\usepackage{xcolor,tcolorbox}
%% https://tex.stackexchange.com/a/126741
\newenvironment{WrapText1}[2][r]
  {\wrapfigure[#2]{#1}{0.5\textwidth}\tcolorbox}
  {\endtcolorbox\endwrapfigure}

\newenvironment{WrapText2}[2][r] 
{\wrapfigure[#2]{#1}{0.5\textwidth}\mdframed[backgroundcolor=gray!20,
skipabove=0pt ,
skipbelow=0pt]}
  {\endmdframed\endwrapfigure}

  \title{}
\begin{document}

\maketitle

\section{Complex networks}

\subsection{Small-world networks}
\label{sec:small-world}
\index{Complex Network!Small-world network}
For many real world phenomena, the average path length $l$ of a network is much 
smaller than that network size $n$, that is $l \ll n$. Such networks are said 
to be characterising the small-world property [1,2]. In 
mathematics, physics and sociology a small-world network (SWN) is a category of 
networks in which most nodes are not neighbours of one another, but most nodes 
can be reached from every other by a small number of \textit{hops} or 
\textit{steps}. D. Watts and S. Strogatz introduced this terminology in 1998 
[5] (also called WS model) that was originated from the famous 
experiment made by Milgram in 1967 [3]. Milgram found that two 
US citizens chosen randomly were connected by an average of six acquaintances.

\subsubsection*{\ding{228} Small-world networks in real life}
small-world networks can be found in many real-world applications, including 
road maps, food chains, electric power grids, metabolite processing networks, 
networks of brain neurons, voter networks, telephone call graphs, and social 
influence networks. These systems comprise of many local links and fewer long 
range \textit{``shortcuts''}, often use with a high degree of local clustering 
but 
relatively small diameter (see more detail below). Networks found in many 
biological and man-made systems are ``small-world networks'', which are highly 
clustered, but the minimum distance between any two randomly chosen nodes in 
the 
graph is short. Thus, studies on SWNs have been interested by many 
researchers in a variety of fields such as mathematics, computer sciences, 
physics, social sciences, etc.

\paragraph{\ding{51}}In a study of Indian physicians [10], they 
have analysed and showed the structure of the Indian railway network (IRN). 
Identifying the stations as nodes of the network and a train which stops at any 
two stations as the edges between the nodes, Sen and co-authors measured the 
average distance between an arbitrary pair of stations and find that it 
depends logarithmically on the total number of stations in the country. While 
from the network point of view this implies the small-world nature of the 
railway 
network, in practice a traveller has to change only a few trains to reach an 
arbitrary destination. This implies that over the years, the railway network 
has 
evolved with the sole aim of becoming fast and economical; eventually its 
structure has become a SWN.

\begin{WrapText2}{20}
 In Goyal's study [6], the principal conditions that a network 
$G$ exhibits \textit{small-world} properties are as the following:
\begin{enumerate}\itemsep1pt \parskip0pt \parsep0pt
\item The number of nodes is very large as compared to the average number 
of links (the average degree), i.e. $n \gg k$
\item The network is integrated; a giant component exists and covers a large 
share of the population.
\item The average distance between nodes $l$ (called characteristic path 
length) in the giant component is small, i.e. $l$ is of order $ln(n)$.
\item The global clustering coefficient is high, i.e. $C \gg k/n$ 
\end{enumerate}
\end{WrapText2}
\subsubsection*{\ding{228} Properties of small-world networks}
Based on the definition of SWN proposed by 
[1] and its extensions such as [1], we have described some 
commonly used properties 
of small-work networks as follows:
\begin{itemize}
 \item the network has SCC.
 \item the local neighbourhood is preserved (as for regular lattices).
 \item the diameter of the network increases logarithmically with the number of 
vertices $n$ (as for random networks).
 \item the clustering coefficients are much larger than those of the random 
networks.
 \item The average length between two points characterising
global properties of the network was found to depend
strongly on the amount of disorder in the network.
\end{itemize}
\paragraph{\ding{51}}Another investigation on Boston subway, Latora and his
collaborators [2] showed that the whole transportation system
MBTA\footnote{Boston underground transportation system} (consists of $n = 124$
stations and $k = 124$ tunnels) and bus turns out to be a small-world with a
slight increase in the cost. This paper showed that a generic closed
transportation system can exhibit the small-world behaviour, substantiating 
the
idea that, in the grand picture, the diffusion of small-world networks can be
interpreted as the need to create networks that are both globally and locally
efficient.
\begin{WrapText2}{15}
\textbf{Power-law distribution}~\\
A power law is a
special kind of mathematical relationship between
two quantities. When the number or frequency of an object or event varies as a
power of some attribute of that object (e.g., its size), the number or frequency
is said to follow a power law. For instance, the number of cities having a
certain population size is found to vary as a power of the size of the
population, and hence follows a power 
law \cite{Clauset2009}.
\end{WrapText2}
\paragraph{\ding{51}}The World Wide Web has a small-world topology as well 
[12]. In this paper, Adamic made a comparison between SWNs
 for sites, and the corresponding random graphs, the subset of \textit{.edu} 
sites was considered. Because the \textit{.edu} subset is significantly 
smaller,
 distances between every node could be computed. $3,456$ of the $11,000$
\textit{.edu} sites formed the largest SCC. In
summary, the largest SCCs of both sites in general
and the subset of \textit{.edu} sites are SWNs with small
average minimum distances.

\paragraph{\ding{51}}In fact, rich-species food webs with a good taxonomic 
resolution display the properties of small-world behaviour [1]. 
Montoya and Sol\'e analysed the four large food webs and compared between real 
webs and randomly generated webs. Consequently, they approved that the 
clustering coefficient of both types is the same average number of links per 
species. One important result is that in all cases, the clustering coefficient 
is clearly larger than the one of the random networks. For the characteristic 
path length, the difference between the random and real case is almost very 
small.

\paragraph{}On the other hand, these scale-free networks own the power-law
behaviour means that most vertices are connected
sparsely, while a few vertices are connected intensively to many
others and play an important role in functionality illustrates the difference 
between 
random and scale-free network.
\end{document}

A:

When you use any environment based on wrapfig you must provide sufficient regular-paragraph text or you will get extremely odd results. The documentation makes this extremely clear: it is a known, well-advertised limitation of the package.
The kinds of things which will cause problems include using a wrapfig environment near:

a section heading;
any environment based on the list environment, including itemize, enumerate, quotation, quote etc.

Putting a list in the figure being wrapped is fine. But wrapping a list around the figure is not going to work.